{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f3320239",
   "metadata": {},
   "source": [
    "# Faster R-CNN on Coco\n",
    "# 🚀 Faster R-CNN：目标检测的里程碑之作\n",
    "\n",
    "Faster R-CNN 是目标检测领域里程碑式的工作，由 Shaoqing Ren、Kaiming He、Ross Girshick 和 Jian Sun 在 2015 年提出（论文标题：[*Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks*](https://arxiv.org/abs/1506.01497)）。它在 R-CNN 系列中承前启后，解决了此前方法的速度瓶颈，同时保持了高精度。\n",
    "\n",
    "---\n",
    "\n",
    "## 🌟 一、核心创新点\n",
    "\n",
    "### 1. 引入 Region Proposal Network (RPN) —— 革命性的区域提议机制\n",
    "\n",
    "- **取代 Selective Search**：在 Fast R-CNN 中，区域提议仍依赖外部算法（如 Selective Search），速度慢且与网络分离。\n",
    "- **RPN 是全卷积网络**：与检测网络共享卷积特征图，直接在特征图上生成候选框（anchor-based），实现“端到端”训练。\n",
    "- **多尺度 anchor 设计**：在每个位置预设多个尺度和长宽比的 anchor，有效应对不同大小目标。\n",
    "\n",
    "### 2. 端到端联合训练\n",
    "\n",
    "- RPN 和 Fast R-CNN 检测头共享卷积特征，通过交替训练或近似联合训练，实现整个网络的端到端优化。\n",
    "- 这是首次将“区域提议 + 目标检测”统一到一个深度网络中，极大提升效率和一致性。\n",
    "\n",
    "### 3. Anchor 机制的开创性应用\n",
    "\n",
    "- Faster R-CNN 首次系统化使用 anchor boxes 作为候选框基础，这一思想深刻影响了后续几乎所有目标检测器（如 SSD、YOLOv2+、RetinaNet 等）。\n",
    "- Anchor 机制让网络学习“相对偏移”，而非绝对坐标，提升泛化能力。\n",
    "\n",
    "### 4. 计算效率大幅提升\n",
    "\n",
    "- 相比 R-CNN（~50s/图）和 Fast R-CNN（~2s/图），Faster R-CNN 在 GPU 上可达 **5–10 FPS**（使用 VGG16），真正迈向“近实时”检测。\n",
    "- 共享卷积特征避免重复计算，是效率飞跃的关键。\n",
    "\n",
    "---\n",
    "\n",
    "## 🌍 二、影响力与历史地位\n",
    "\n",
    "### 1. 奠定 Two-Stage 检测器的主流架构\n",
    "\n",
    "- 成为后续如 **Mask R-CNN**、**Cascade R-CNN**、**Libra R-CNN** 等高性能检测器的基础框架。\n",
    "- “RPN + RoI Pooling + 分类/回归头” 成为标准范式。\n",
    "\n",
    "### 2. 推动目标检测进入深度学习黄金时代\n",
    "\n",
    "- 在 **PASCAL VOC**、**MS COCO** 等权威数据集上长期霸榜，证明深度学习在检测任务上的强大能力。\n",
    "- 2015 年 COCO 检测冠军即基于 Faster R-CNN，引发学术界和工业界广泛关注。\n",
    "\n",
    "### 3. 工业落地广泛\n",
    "\n",
    "- 被广泛应用于 **自动驾驶**（如 Waymo）、**机器人视觉**、**安防监控**、**医疗影像**等领域。\n",
    "- 成为许多商业检测系统的底层架构。\n",
    "\n",
    "### 4. 启发 One-Stage 方法的发展\n",
    "\n",
    "- 尽管是 Two-Stage 方法，但其 **anchor 机制** 被 SSD、YOLOv2 等 One-Stage 方法借鉴，推动整个领域演进。\n",
    "- 后续很多改进（如 FPN、ATSS、IoU Loss）都建立在 Faster R-CNN 框架之上。\n",
    "\n",
    "### 5. 学术引用与开源生态\n",
    "\n",
    "- 截至 2024 年，论文引用量 **超 4 万次**（Google Scholar），是计算机视觉领域被引最高的论文之一。\n",
    "- 官方代码和众多复现版本（如 **Detectron**、**mmdetection**）极大推动社区发展。\n",
    "\n",
    "---\n",
    "\n",
    "## ✅ 总结一句话\n",
    "\n",
    "> **Faster R-CNN 通过引入 RPN 和 anchor 机制，首次实现端到端可训练的高效目标检测框架，不仅大幅提速，更奠定了现代目标检测的基础架构，是深度学习目标检测发展史上的分水岭之作。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1e2f4d9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use device:  cuda\n"
     ]
    }
   ],
   "source": [
    "# 自动重新加载外部module，使得修改代码之后无需重新import\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from hdd.device.utils import get_device\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from pathlib import Path\n",
    "\n",
    "# 设置训练数据的路径\n",
    "DATA_ROOT = Path(\"~/workspace/hands-dirty-on-dl/dataset\").expanduser()\n",
    "# 设置预训练模型参数路径\n",
    "TORCH_HUB_PATH = Path(\"~/workspace/hands-dirty-on-dl/pretrained_models\").expanduser()\n",
    "torch.hub.set_dir(TORCH_HUB_PATH)\n",
    "# 挑选最合适的训练设备\n",
    "DEVICE = get_device([\"cuda\", \"cpu\"])\n",
    "print(\"Use device: \", DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2df4e30f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading annotations into memory...\n",
      "Done (t=5.99s)\n",
      "creating index...\n",
      "index created!\n",
      "loading annotations into memory...\n",
      "Done (t=0.92s)\n",
      "creating index...\n",
      "index created!\n",
      "Train dataset size: 117266\n",
      "Val dataset size: 5000\n",
      "----------------------------------------------------\n",
      "type(img) = <class 'torch.Tensor'>\n",
      "type(target) = <class 'dict'>\n",
      "target.keys() = dict_keys(['image_id', 'boxes', 'labels'])\n",
      "type(target['boxes']) = <class 'torch.Tensor'>\n",
      "type(target['labels']) = <class 'torch.Tensor'>\n"
     ]
    }
   ],
   "source": [
    "from torchvision import models, datasets, tv_tensors\n",
    "from torchvision.transforms import v2\n",
    "from torchvision.datasets import wrap_dataset_for_transforms_v2\n",
    "from hdd.scripts.detection.coco_utils import _coco_remove_images_without_annotations\n",
    "\n",
    "COCO_ROOT = Path(DATA_ROOT).expanduser() / \"coco\"\n",
    "\n",
    "\n",
    "def get_coco(train: bool, transforms=None):\n",
    "    image_path = f\"{COCO_ROOT}/train2017\" if train else f\"{COCO_ROOT}/val2017\"\n",
    "    annotation_file_path = (\n",
    "        f\"{COCO_ROOT}/annotations/instances_train2017.json\"\n",
    "        if train\n",
    "        else f\"{COCO_ROOT}/annotations/instances_val2017.json\"\n",
    "    )\n",
    "\n",
    "    dataset = datasets.CocoDetection(\n",
    "        root=image_path,\n",
    "        annFile=annotation_file_path,\n",
    "        transforms=transforms,\n",
    "    )\n",
    "    target_keys = [\"boxes\", \"labels\", \"image_id\"]\n",
    "    dataset = wrap_dataset_for_transforms_v2(dataset, target_keys=target_keys)\n",
    "\n",
    "    if train:\n",
    "        dataset = _coco_remove_images_without_annotations(dataset)\n",
    "    return dataset\n",
    "\n",
    "\n",
    "train_transforms = v2.Compose(\n",
    "    [\n",
    "        v2.ToImage(),\n",
    "        v2.RandomHorizontalFlip(p=0.5),\n",
    "        v2.ToDtype(torch.float32, scale=True),\n",
    "        v2.ConvertBoundingBoxFormat(tv_tensors.BoundingBoxFormat.XYXY),\n",
    "        v2.SanitizeBoundingBoxes(),\n",
    "        v2.ToPureTensor(),\n",
    "    ]\n",
    ")\n",
    "\n",
    "val_transforms = v2.Compose(\n",
    "    [\n",
    "        v2.ToImage(),\n",
    "        v2.ToDtype(torch.float32, scale=True),\n",
    "        v2.ConvertBoundingBoxFormat(tv_tensors.BoundingBoxFormat.XYXY),\n",
    "        v2.ToPureTensor(),\n",
    "    ]\n",
    ")\n",
    "\n",
    "train_dataset = get_coco(train=True, transforms=train_transforms)\n",
    "val_dataset = get_coco(train=False, transforms=val_transforms)\n",
    "print(f\"Train dataset size: {len(train_dataset)}\")\n",
    "print(f\"Val dataset size: {len(val_dataset)}\")\n",
    "print(\"----------------------------------------------------\")\n",
    "img, target = val_dataset[0]\n",
    "print(f\"{type(img) = }\\n{type(target) = }\\n{target.keys() = }\")\n",
    "print(f\"{type(target['boxes']) = }\\n{type(target['labels']) = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db7cbc27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using [0, 0.5, 0.6299605249474366, 0.7937005259840997, 1.0, 1.2599210498948732, 1.5874010519681994, 2.0, inf] as bins for aspect ratio quantization\n",
      "Count of instances per bin: [  104   982 24236  2332  8225 74466  5763  1158]\n"
     ]
    }
   ],
   "source": [
    "from hdd.scripts.detection.group_by_aspect_ratio import (\n",
    "    GroupedBatchSampler,\n",
    "    create_aspect_ratio_groups,\n",
    ")\n",
    "\n",
    "train_batch_size = 16\n",
    "train_sampler = torch.utils.data.RandomSampler(train_dataset)\n",
    "group_ids = create_aspect_ratio_groups(train_dataset, k=3)\n",
    "train_batch_sampler = GroupedBatchSampler(train_sampler, group_ids, train_batch_size)\n",
    "val_sampler = torch.utils.data.SequentialSampler(val_transforms)\n",
    "train_dataloader = torch.utils.data.DataLoader(\n",
    "    train_dataset,\n",
    "    batch_size=train_batch_size,\n",
    "    # We need a custom collation function here, since the object detection\n",
    "    # models expect a sequence of images and target dictionaries. The default\n",
    "    # collation function tries to torch.stack() the individual elements,\n",
    "    # which fails in general for object detection, because the number of bounding\n",
    "    # boxes varies between the images of the same batch.\n",
    "    collate_fn=lambda batch: tuple(zip(*batch)),\n",
    "    shuffle=True,\n",
    "    num_workers=8,\n",
    ")\n",
    "\n",
    "val_dataloader = torch.utils.data.DataLoader(\n",
    "    val_dataset,\n",
    "    batch_size=train_batch_size,\n",
    "    collate_fn=lambda batch: tuple(zip(*batch)),\n",
    "    num_workers=4,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14005373",
   "metadata": {},
   "source": [
    "### Modifying the model to add a different backbone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1bc60ef6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tf/anaconda3/envs/pytorch-cu124/lib/python3.11/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/home/tf/anaconda3/envs/pytorch-cu124/lib/python3.11/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MobileNet_V2_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V2_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "import torchvision\n",
    "from torchvision.models.detection import FasterRCNN\n",
    "from torchvision.models.detection.rpn import AnchorGenerator\n",
    "from torchvision.models import MobileNet_V2_Weights\n",
    "from torchvision.models.detection.backbone_utils import mobilenet_backbone\n",
    "\n",
    "# load a pre-trained model for classification and return only the features\n",
    "# mobilenet_backbone会将BN Layer冻结且可指定可训练的层数\n",
    "backbone = mobilenet_backbone(\n",
    "    backbone_name=\"mobilenet_v2\", fpn=False, pretrained=True, trainable_layers=3\n",
    ")\n",
    "\n",
    "# let's make the RPN generate 5 x 3 anchors per spatial\n",
    "# location, with 5 different sizes and 3 different aspect\n",
    "# ratios. We have a Tuple[Tuple[int]] because each feature\n",
    "# map could potentially have different sizes and\n",
    "# aspect ratios\n",
    "anchor_generator = AnchorGenerator(\n",
    "    sizes=((32, 64, 128, 256, 512),), aspect_ratios=((0.5, 1.0, 2.0),)\n",
    ")\n",
    "\n",
    "# let's define what are the feature maps that we will\n",
    "# use to perform the region of interest cropping, as well as\n",
    "# the size of the crop after rescaling.\n",
    "# if your backbone returns a Tensor, featmap_names is expected to\n",
    "# be [0]. More generally, the backbone should return an\n",
    "# ``OrderedDict[Tensor]``, and in ``featmap_names`` you can choose which\n",
    "# feature maps to use.\n",
    "roi_pooler = torchvision.ops.MultiScaleRoIAlign(\n",
    "    featmap_names=[\"0\"], output_size=7, sampling_ratio=2\n",
    ")\n",
    "\n",
    "# put the pieces together inside a Faster-RCNN model\n",
    "num_classes = 91\n",
    "model = FasterRCNN(\n",
    "    backbone,\n",
    "    num_classes=num_classes,\n",
    "    rpn_anchor_generator=anchor_generator,\n",
    "    box_roi_pool=roi_pooler,\n",
    ")\n",
    "model = model.to(DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e0234ffe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hdd.train.warmup_scheduler import GradualWarmupScheduler\n",
    "\n",
    "weight_decay = 1e-4\n",
    "learning_rate = 2e-2\n",
    "max_iterations = 50_000\n",
    "norm_params, other_params = torchvision.ops._utils.split_normalization_params(model)\n",
    "# Do not apply weight decay to norm parameters\n",
    "parameters = [\n",
    "    {\"params\": norm_params, \"weight_decay\": 0},\n",
    "    {\"params\": other_params, \"weight_decay\": weight_decay},\n",
    "]\n",
    "# optimizer = torch.optim.AdamW(parameters, lr=learning_rate, betas=[0.9, 0.95], weight_decay = weight_decay)\n",
    "optimizer = torch.optim.SGD(\n",
    "    parameters,\n",
    "    lr=learning_rate,\n",
    "    momentum=0.9,\n",
    "    weight_decay=weight_decay,\n",
    ")\n",
    "base_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
    "    optimizer, max_iterations, eta_min=1e-6\n",
    ")\n",
    "scheduler = GradualWarmupScheduler(\n",
    "    optimizer,\n",
    "    multiplier=1.0,\n",
    "    total_epoch=2000,\n",
    "    after_scheduler=base_scheduler,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ffce98ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hdd.scripts.detection import utils\n",
    "import math\n",
    "import sys\n",
    "\n",
    "\n",
    "def train_one_epoch(\n",
    "    model, optimizer, scheduler, data_loader, device, epoch, print_freq, scaler=None\n",
    ") -> utils.MetricLogger:\n",
    "    model.train()\n",
    "    metric_logger = utils.MetricLogger(delimiter=\"  \")\n",
    "    metric_logger.add_meter(\"lr\", utils.SmoothedValue(window_size=1, fmt=\"{value:.6f}\"))\n",
    "    header = f\"Epoch: [{epoch}]\"\n",
    "\n",
    "    for images, targets in metric_logger.log_every(data_loader, print_freq, header):\n",
    "        images = list(image.to(device) for image in images)\n",
    "        targets = [\n",
    "            {\n",
    "                k: v.to(device) if isinstance(v, torch.Tensor) else v\n",
    "                for k, v in t.items()\n",
    "            }\n",
    "            for t in targets\n",
    "        ]\n",
    "        with torch.amp.autocast(enabled=scaler is not None, device_type=\"cuda\"):\n",
    "            loss_dict = model(images, targets)\n",
    "            losses = sum(loss for loss in loss_dict.values())\n",
    "\n",
    "        # reduce losses over all GPUs for logging purposes\n",
    "        loss_dict_reduced = utils.reduce_dict(loss_dict)\n",
    "        losses_reduced = sum(loss for loss in loss_dict_reduced.values())\n",
    "\n",
    "        loss_value = losses_reduced.item()\n",
    "\n",
    "        if not math.isfinite(loss_value):\n",
    "            print(f\"Loss is {loss_value}, stopping training\")\n",
    "            print(loss_dict_reduced)\n",
    "            sys.exit(1)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        if scaler is not None:\n",
    "            scaler.scale(losses).backward()\n",
    "            scaler.step(optimizer)\n",
    "            scaler.update()\n",
    "        else:\n",
    "            losses.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "        scheduler.step()\n",
    "\n",
    "        metric_logger.update(loss=losses_reduced, **loss_dict_reduced)\n",
    "        metric_logger.update(lr=optimizer.param_groups[0][\"lr\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a75459ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from hdd.scripts.detection.coco_utils import get_coco_api_from_dataset\n",
    "from hdd.scripts.detection.coco_eval import CocoEvaluator\n",
    "\n",
    "\n",
    "@torch.inference_mode()\n",
    "def evaluate(model, data_loader, device):\n",
    "    n_threads = torch.get_num_threads()\n",
    "    # FIXME remove this and make paste_masks_in_image run on the GPU\n",
    "    cpu_device = torch.device(\"cpu\")\n",
    "    model.eval()\n",
    "    metric_logger = utils.MetricLogger(delimiter=\"  \")\n",
    "    header = \"Test:\"\n",
    "\n",
    "    coco = get_coco_api_from_dataset(data_loader.dataset)\n",
    "    iou_types = [\"bbox\"]\n",
    "    coco_evaluator = CocoEvaluator(coco, iou_types)\n",
    "\n",
    "    for images, targets in metric_logger.log_every(data_loader, 1000, header):\n",
    "        images = list(img.to(device) for img in images)\n",
    "\n",
    "        if torch.cuda.is_available():\n",
    "            torch.cuda.synchronize()\n",
    "        model_time = time.time()\n",
    "        outputs = model(images)\n",
    "\n",
    "        outputs = [{k: v.to(cpu_device) for k, v in t.items()} for t in outputs]\n",
    "        model_time = time.time() - model_time\n",
    "\n",
    "        res = {target[\"image_id\"]: output for target, output in zip(targets, outputs)}\n",
    "        evaluator_time = time.time()\n",
    "        coco_evaluator.update(res)\n",
    "        evaluator_time = time.time() - evaluator_time\n",
    "        metric_logger.update(model_time=model_time, evaluator_time=evaluator_time)\n",
    "\n",
    "    print(\"Averaged stats:\", metric_logger)\n",
    "\n",
    "    # accumulate predictions from all images\n",
    "    coco_evaluator.accumulate()\n",
    "    coco_evaluator.summarize()\n",
    "    return coco_evaluator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9b885666",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max epochs: 6\n",
      "Epoch: [0]  [   0/7330]  eta: 2:31:23  lr: 0.000010  loss: 5.4188 (5.4188)  loss_classifier: 4.5156 (4.5156)  loss_box_reg: 0.0496 (0.0496)  loss_objectness: 0.6914 (0.6914)  loss_rpn_box_reg: 0.1622 (0.1622)  time: 1.2392  data: 0.5840  max mem: 6803\n",
      "Epoch: [0]  [ 100/7330]  eta: 0:26:36  lr: 0.001010  loss: 1.2175 (2.5699)  loss_classifier: 0.6038 (1.8218)  loss_box_reg: 0.2458 (0.1291)  loss_objectness: 0.2744 (0.5071)  loss_rpn_box_reg: 0.1011 (0.1118)  time: 0.1999  data: 0.0061  max mem: 7658\n",
      "Epoch: [0]  [ 200/7330]  eta: 0:25:31  lr: 0.002010  loss: 0.9174 (1.8276)  loss_classifier: 0.4268 (1.1792)  loss_box_reg: 0.1735 (0.1737)  loss_objectness: 0.1901 (0.3655)  loss_rpn_box_reg: 0.1117 (0.1092)  time: 0.2145  data: 0.0066  max mem: 7660\n",
      "Epoch: [0]  [ 300/7330]  eta: 0:25:07  lr: 0.003010  loss: 0.8785 (1.5298)  loss_classifier: 0.4134 (0.9368)  loss_box_reg: 0.1753 (0.1799)  loss_objectness: 0.1896 (0.3066)  loss_rpn_box_reg: 0.0996 (0.1065)  time: 0.2144  data: 0.0076  max mem: 7660\n",
      "Epoch: [0]  [ 400/7330]  eta: 0:24:48  lr: 0.004010  loss: 0.8158 (1.3730)  loss_classifier: 0.3843 (0.8097)  loss_box_reg: 0.1761 (0.1846)  loss_objectness: 0.1567 (0.2731)  loss_rpn_box_reg: 0.0849 (0.1057)  time: 0.2181  data: 0.0072  max mem: 7661\n",
      "Epoch: [0]  [ 500/7330]  eta: 0:24:28  lr: 0.005010  loss: 0.9682 (1.2731)  loss_classifier: 0.4239 (0.7310)  loss_box_reg: 0.2320 (0.1889)  loss_objectness: 0.1464 (0.2499)  loss_rpn_box_reg: 0.0893 (0.1033)  time: 0.2109  data: 0.0070  max mem: 7661\n",
      "Epoch: [0]  [ 600/7330]  eta: 0:24:09  lr: 0.006010  loss: 0.7934 (1.2068)  loss_classifier: 0.3652 (0.6780)  loss_box_reg: 0.2023 (0.1927)  loss_objectness: 0.1443 (0.2332)  loss_rpn_box_reg: 0.0989 (0.1029)  time: 0.2163  data: 0.0062  max mem: 7661\n",
      "Epoch: [0]  [ 700/7330]  eta: 0:23:46  lr: 0.007010  loss: 0.8167 (1.1604)  loss_classifier: 0.3778 (0.6402)  loss_box_reg: 0.1889 (0.1967)  loss_objectness: 0.1420 (0.2215)  loss_rpn_box_reg: 0.0942 (0.1021)  time: 0.2138  data: 0.0062  max mem: 7663\n",
      "Epoch: [0]  [ 800/7330]  eta: 0:23:26  lr: 0.008010  loss: 0.9432 (1.1236)  loss_classifier: 0.4540 (0.6105)  loss_box_reg: 0.2548 (0.2007)  loss_objectness: 0.1382 (0.2109)  loss_rpn_box_reg: 0.0985 (0.1014)  time: 0.2191  data: 0.0063  max mem: 7663\n",
      "Epoch: [0]  [ 900/7330]  eta: 0:23:08  lr: 0.009010  loss: 0.7957 (1.0913)  loss_classifier: 0.3656 (0.5853)  loss_box_reg: 0.2291 (0.2029)  loss_objectness: 0.1179 (0.2023)  loss_rpn_box_reg: 0.0823 (0.1008)  time: 0.2200  data: 0.0064  max mem: 7663\n",
      "Epoch: [0]  [1000/7330]  eta: 0:22:48  lr: 0.010010  loss: 0.7997 (1.0628)  loss_classifier: 0.3658 (0.5633)  loss_box_reg: 0.2400 (0.2056)  loss_objectness: 0.1173 (0.1939)  loss_rpn_box_reg: 0.0843 (0.1000)  time: 0.2164  data: 0.0063  max mem: 7663\n",
      "Epoch: [0]  [1100/7330]  eta: 0:22:26  lr: 0.011010  loss: 0.8066 (1.0409)  loss_classifier: 0.3755 (0.5457)  loss_box_reg: 0.2406 (0.2080)  loss_objectness: 0.1145 (0.1875)  loss_rpn_box_reg: 0.0885 (0.0997)  time: 0.2168  data: 0.0065  max mem: 7663\n",
      "Epoch: [0]  [1200/7330]  eta: 0:22:07  lr: 0.012010  loss: 0.7773 (1.0247)  loss_classifier: 0.3519 (0.5319)  loss_box_reg: 0.2374 (0.2109)  loss_objectness: 0.1064 (0.1823)  loss_rpn_box_reg: 0.0865 (0.0996)  time: 0.2213  data: 0.0062  max mem: 7663\n",
      "Epoch: [0]  [1300/7330]  eta: 0:21:46  lr: 0.013010  loss: 0.8187 (1.0099)  loss_classifier: 0.3615 (0.5194)  loss_box_reg: 0.2263 (0.2131)  loss_objectness: 0.1128 (0.1780)  loss_rpn_box_reg: 0.0906 (0.0993)  time: 0.2202  data: 0.0064  max mem: 7663\n",
      "Epoch: [0]  [1400/7330]  eta: 0:21:24  lr: 0.014010  loss: 0.8450 (0.9966)  loss_classifier: 0.3685 (0.5084)  loss_box_reg: 0.2454 (0.2149)  loss_objectness: 0.1331 (0.1742)  loss_rpn_box_reg: 0.0959 (0.0992)  time: 0.2161  data: 0.0065  max mem: 7663\n",
      "Epoch: [0]  [1500/7330]  eta: 0:21:04  lr: 0.015010  loss: 0.8410 (0.9848)  loss_classifier: 0.3679 (0.4987)  loss_box_reg: 0.2564 (0.2166)  loss_objectness: 0.1154 (0.1703)  loss_rpn_box_reg: 0.0878 (0.0992)  time: 0.2204  data: 0.0067  max mem: 7663\n",
      "Epoch: [0]  [1600/7330]  eta: 0:20:42  lr: 0.016010  loss: 0.7729 (0.9735)  loss_classifier: 0.3486 (0.4893)  loss_box_reg: 0.2334 (0.2181)  loss_objectness: 0.0997 (0.1670)  loss_rpn_box_reg: 0.0863 (0.0991)  time: 0.2134  data: 0.0063  max mem: 7663\n",
      "Epoch: [0]  [1700/7330]  eta: 0:20:21  lr: 0.017010  loss: 0.7339 (0.9624)  loss_classifier: 0.3377 (0.4807)  loss_box_reg: 0.2244 (0.2193)  loss_objectness: 0.0997 (0.1637)  loss_rpn_box_reg: 0.0760 (0.0986)  time: 0.2199  data: 0.0061  max mem: 7663\n",
      "Epoch: [0]  [1800/7330]  eta: 0:20:01  lr: 0.018010  loss: 0.7254 (0.9521)  loss_classifier: 0.3190 (0.4732)  loss_box_reg: 0.2198 (0.2202)  loss_objectness: 0.1047 (0.1607)  loss_rpn_box_reg: 0.0833 (0.0982)  time: 0.2237  data: 0.0068  max mem: 7663\n",
      "Epoch: [0]  [1900/7330]  eta: 0:19:40  lr: 0.019010  loss: 0.7480 (0.9427)  loss_classifier: 0.3221 (0.4661)  loss_box_reg: 0.2172 (0.2210)  loss_objectness: 0.1077 (0.1578)  loss_rpn_box_reg: 0.0754 (0.0978)  time: 0.2183  data: 0.0065  max mem: 7663\n",
      "Epoch: [0]  [2000/7330]  eta: 0:19:19  lr: 0.020000  loss: 0.7798 (0.9356)  loss_classifier: 0.3462 (0.4605)  loss_box_reg: 0.2457 (0.2222)  loss_objectness: 0.1009 (0.1554)  loss_rpn_box_reg: 0.0854 (0.0976)  time: 0.2202  data: 0.0061  max mem: 7663\n",
      "Epoch: [0]  [2100/7330]  eta: 0:18:58  lr: 0.020000  loss: 0.6887 (0.9273)  loss_classifier: 0.3048 (0.4544)  loss_box_reg: 0.2066 (0.2227)  loss_objectness: 0.0938 (0.1530)  loss_rpn_box_reg: 0.0843 (0.0972)  time: 0.2155  data: 0.0063  max mem: 7663\n",
      "Epoch: [0]  [2200/7330]  eta: 0:18:36  lr: 0.019999  loss: 0.7243 (0.9203)  loss_classifier: 0.3350 (0.4490)  loss_box_reg: 0.2618 (0.2234)  loss_objectness: 0.1001 (0.1509)  loss_rpn_box_reg: 0.0823 (0.0970)  time: 0.2189  data: 0.0060  max mem: 7663\n",
      "Epoch: [0]  [2300/7330]  eta: 0:18:14  lr: 0.019998  loss: 0.7415 (0.9132)  loss_classifier: 0.3329 (0.4440)  loss_box_reg: 0.2347 (0.2238)  loss_objectness: 0.1002 (0.1488)  loss_rpn_box_reg: 0.0948 (0.0967)  time: 0.2232  data: 0.0063  max mem: 7663\n",
      "Epoch: [0]  [2400/7330]  eta: 0:17:53  lr: 0.019997  loss: 0.7439 (0.9066)  loss_classifier: 0.3280 (0.4391)  loss_box_reg: 0.2360 (0.2243)  loss_objectness: 0.0968 (0.1468)  loss_rpn_box_reg: 0.0820 (0.0964)  time: 0.2203  data: 0.0061  max mem: 7663\n",
      "Epoch: [0]  [2500/7330]  eta: 0:17:31  lr: 0.019995  loss: 0.7474 (0.9003)  loss_classifier: 0.3190 (0.4346)  loss_box_reg: 0.2259 (0.2248)  loss_objectness: 0.0958 (0.1449)  loss_rpn_box_reg: 0.0933 (0.0961)  time: 0.2165  data: 0.0061  max mem: 7663\n",
      "Epoch: [0]  [2600/7330]  eta: 0:17:10  lr: 0.019993  loss: 0.7062 (0.8942)  loss_classifier: 0.3068 (0.4301)  loss_box_reg: 0.2183 (0.2251)  loss_objectness: 0.0858 (0.1431)  loss_rpn_box_reg: 0.0841 (0.0958)  time: 0.2252  data: 0.0059  max mem: 7663\n",
      "Epoch: [0]  [2700/7330]  eta: 0:16:48  lr: 0.019990  loss: 0.7600 (0.8896)  loss_classifier: 0.3349 (0.4267)  loss_box_reg: 0.2400 (0.2259)  loss_objectness: 0.0893 (0.1415)  loss_rpn_box_reg: 0.0789 (0.0955)  time: 0.2177  data: 0.0062  max mem: 7663\n",
      "Epoch: [0]  [2800/7330]  eta: 0:16:26  lr: 0.019987  loss: 0.7472 (0.8840)  loss_classifier: 0.3416 (0.4229)  loss_box_reg: 0.2267 (0.2261)  loss_objectness: 0.0983 (0.1399)  loss_rpn_box_reg: 0.0755 (0.0951)  time: 0.2141  data: 0.0060  max mem: 7663\n",
      "Epoch: [0]  [2900/7330]  eta: 0:16:05  lr: 0.019984  loss: 0.7646 (0.8794)  loss_classifier: 0.3413 (0.4196)  loss_box_reg: 0.2470 (0.2266)  loss_objectness: 0.0944 (0.1385)  loss_rpn_box_reg: 0.0816 (0.0947)  time: 0.2157  data: 0.0060  max mem: 7663\n",
      "Epoch: [0]  [3000/7330]  eta: 0:15:43  lr: 0.019980  loss: 0.7126 (0.8746)  loss_classifier: 0.3071 (0.4163)  loss_box_reg: 0.2421 (0.2269)  loss_objectness: 0.0897 (0.1371)  loss_rpn_box_reg: 0.0853 (0.0942)  time: 0.2204  data: 0.0060  max mem: 7663\n",
      "Epoch: [0]  [3100/7330]  eta: 0:15:21  lr: 0.019976  loss: 0.7159 (0.8701)  loss_classifier: 0.2997 (0.4132)  loss_box_reg: 0.2343 (0.2271)  loss_objectness: 0.0878 (0.1358)  loss_rpn_box_reg: 0.0798 (0.0940)  time: 0.2166  data: 0.0059  max mem: 7663\n",
      "Epoch: [0]  [3200/7330]  eta: 0:14:59  lr: 0.019972  loss: 0.6848 (0.8654)  loss_classifier: 0.2890 (0.4101)  loss_box_reg: 0.2097 (0.2270)  loss_objectness: 0.0770 (0.1344)  loss_rpn_box_reg: 0.0898 (0.0939)  time: 0.2163  data: 0.0061  max mem: 7663\n",
      "Epoch: [0]  [3300/7330]  eta: 0:14:37  lr: 0.019967  loss: 0.7167 (0.8613)  loss_classifier: 0.3086 (0.4072)  loss_box_reg: 0.2196 (0.2272)  loss_objectness: 0.0964 (0.1333)  loss_rpn_box_reg: 0.0879 (0.0936)  time: 0.2199  data: 0.0066  max mem: 7663\n",
      "Epoch: [0]  [3400/7330]  eta: 0:14:15  lr: 0.019961  loss: 0.6865 (0.8574)  loss_classifier: 0.3035 (0.4045)  loss_box_reg: 0.2402 (0.2273)  loss_objectness: 0.0849 (0.1321)  loss_rpn_box_reg: 0.0779 (0.0935)  time: 0.2209  data: 0.0062  max mem: 7663\n",
      "Epoch: [0]  [3500/7330]  eta: 0:13:54  lr: 0.019956  loss: 0.6627 (0.8534)  loss_classifier: 0.2887 (0.4018)  loss_box_reg: 0.2068 (0.2274)  loss_objectness: 0.0880 (0.1309)  loss_rpn_box_reg: 0.0806 (0.0933)  time: 0.2196  data: 0.0061  max mem: 7663\n",
      "Epoch: [0]  [3600/7330]  eta: 0:13:32  lr: 0.019950  loss: 0.6823 (0.8496)  loss_classifier: 0.2951 (0.3994)  loss_box_reg: 0.2182 (0.2275)  loss_objectness: 0.0808 (0.1298)  loss_rpn_box_reg: 0.0755 (0.0930)  time: 0.2185  data: 0.0063  max mem: 7663\n",
      "Epoch: [0]  [3700/7330]  eta: 0:13:10  lr: 0.019943  loss: 0.6757 (0.8461)  loss_classifier: 0.2886 (0.3970)  loss_box_reg: 0.2243 (0.2277)  loss_objectness: 0.0815 (0.1287)  loss_rpn_box_reg: 0.0865 (0.0927)  time: 0.2205  data: 0.0060  max mem: 7663\n",
      "Epoch: [0]  [3800/7330]  eta: 0:12:48  lr: 0.019936  loss: 0.6067 (0.8420)  loss_classifier: 0.2557 (0.3944)  loss_box_reg: 0.1971 (0.2276)  loss_objectness: 0.0777 (0.1276)  loss_rpn_box_reg: 0.0576 (0.0924)  time: 0.2209  data: 0.0060  max mem: 7663\n",
      "Epoch: [0]  [3900/7330]  eta: 0:12:27  lr: 0.019929  loss: 0.6919 (0.8382)  loss_classifier: 0.3004 (0.3919)  loss_box_reg: 0.2108 (0.2274)  loss_objectness: 0.0903 (0.1267)  loss_rpn_box_reg: 0.0724 (0.0922)  time: 0.2198  data: 0.0062  max mem: 7663\n",
      "Epoch: [0]  [4000/7330]  eta: 0:12:05  lr: 0.019921  loss: 0.7414 (0.8350)  loss_classifier: 0.3125 (0.3897)  loss_box_reg: 0.2381 (0.2274)  loss_objectness: 0.0911 (0.1258)  loss_rpn_box_reg: 0.0825 (0.0921)  time: 0.2193  data: 0.0061  max mem: 7663\n",
      "Epoch: [0]  [4100/7330]  eta: 0:11:43  lr: 0.019913  loss: 0.6474 (0.8311)  loss_classifier: 0.2715 (0.3872)  loss_box_reg: 0.2041 (0.2271)  loss_objectness: 0.0811 (0.1249)  loss_rpn_box_reg: 0.0767 (0.0918)  time: 0.2184  data: 0.0059  max mem: 7663\n",
      "Epoch: [0]  [4200/7330]  eta: 0:11:21  lr: 0.019905  loss: 0.6685 (0.8282)  loss_classifier: 0.2966 (0.3853)  loss_box_reg: 0.1958 (0.2270)  loss_objectness: 0.0912 (0.1242)  loss_rpn_box_reg: 0.0819 (0.0917)  time: 0.2172  data: 0.0060  max mem: 7663\n",
      "Epoch: [0]  [4300/7330]  eta: 0:11:00  lr: 0.019896  loss: 0.6998 (0.8253)  loss_classifier: 0.3032 (0.3834)  loss_box_reg: 0.2403 (0.2271)  loss_objectness: 0.0852 (0.1233)  loss_rpn_box_reg: 0.0715 (0.0915)  time: 0.2201  data: 0.0061  max mem: 7663\n",
      "Epoch: [0]  [4400/7330]  eta: 0:10:38  lr: 0.019887  loss: 0.6424 (0.8224)  loss_classifier: 0.2809 (0.3815)  loss_box_reg: 0.2156 (0.2271)  loss_objectness: 0.0853 (0.1225)  loss_rpn_box_reg: 0.0613 (0.0912)  time: 0.2179  data: 0.0061  max mem: 7663\n",
      "Epoch: [0]  [4500/7330]  eta: 0:10:16  lr: 0.019877  loss: 0.6596 (0.8191)  loss_classifier: 0.2932 (0.3794)  loss_box_reg: 0.1921 (0.2270)  loss_objectness: 0.0856 (0.1217)  loss_rpn_box_reg: 0.0785 (0.0910)  time: 0.2202  data: 0.0063  max mem: 7663\n",
      "Epoch: [0]  [4600/7330]  eta: 0:09:54  lr: 0.019867  loss: 0.6791 (0.8163)  loss_classifier: 0.2902 (0.3777)  loss_box_reg: 0.2192 (0.2271)  loss_objectness: 0.0866 (0.1209)  loss_rpn_box_reg: 0.0864 (0.0907)  time: 0.2149  data: 0.0064  max mem: 7663\n",
      "Epoch: [0]  [4700/7330]  eta: 0:09:32  lr: 0.019856  loss: 0.6406 (0.8134)  loss_classifier: 0.2821 (0.3759)  loss_box_reg: 0.2032 (0.2270)  loss_objectness: 0.0843 (0.1202)  loss_rpn_box_reg: 0.0798 (0.0904)  time: 0.2119  data: 0.0063  max mem: 7663\n",
      "Epoch: [0]  [4800/7330]  eta: 0:09:11  lr: 0.019846  loss: 0.6866 (0.8105)  loss_classifier: 0.2888 (0.3741)  loss_box_reg: 0.2127 (0.2268)  loss_objectness: 0.0902 (0.1194)  loss_rpn_box_reg: 0.0755 (0.0901)  time: 0.2149  data: 0.0060  max mem: 7663\n",
      "Epoch: [0]  [4900/7330]  eta: 0:08:49  lr: 0.019834  loss: 0.6503 (0.8080)  loss_classifier: 0.2767 (0.3725)  loss_box_reg: 0.2060 (0.2268)  loss_objectness: 0.0763 (0.1187)  loss_rpn_box_reg: 0.0852 (0.0900)  time: 0.2189  data: 0.0063  max mem: 7663\n",
      "Epoch: [0]  [5000/7330]  eta: 0:08:27  lr: 0.019823  loss: 0.6311 (0.8054)  loss_classifier: 0.2712 (0.3709)  loss_box_reg: 0.2150 (0.2266)  loss_objectness: 0.0791 (0.1180)  loss_rpn_box_reg: 0.0806 (0.0899)  time: 0.2220  data: 0.0062  max mem: 7663\n",
      "Epoch: [0]  [5100/7330]  eta: 0:08:06  lr: 0.019811  loss: 0.7168 (0.8035)  loss_classifier: 0.3145 (0.3696)  loss_box_reg: 0.2352 (0.2268)  loss_objectness: 0.0817 (0.1174)  loss_rpn_box_reg: 0.0796 (0.0897)  time: 0.2242  data: 0.0063  max mem: 7663\n",
      "Epoch: [0]  [5200/7330]  eta: 0:07:44  lr: 0.019799  loss: 0.6684 (0.8013)  loss_classifier: 0.3087 (0.3681)  loss_box_reg: 0.2342 (0.2267)  loss_objectness: 0.0880 (0.1169)  loss_rpn_box_reg: 0.0828 (0.0896)  time: 0.2176  data: 0.0066  max mem: 7663\n",
      "Epoch: [0]  [5300/7330]  eta: 0:07:22  lr: 0.019786  loss: 0.6665 (0.7989)  loss_classifier: 0.3005 (0.3666)  loss_box_reg: 0.2189 (0.2265)  loss_objectness: 0.0801 (0.1163)  loss_rpn_box_reg: 0.0734 (0.0895)  time: 0.2175  data: 0.0061  max mem: 7663\n",
      "Epoch: [0]  [5400/7330]  eta: 0:07:00  lr: 0.019773  loss: 0.6665 (0.7964)  loss_classifier: 0.2848 (0.3651)  loss_box_reg: 0.2134 (0.2264)  loss_objectness: 0.0913 (0.1156)  loss_rpn_box_reg: 0.0744 (0.0892)  time: 0.2176  data: 0.0061  max mem: 7663\n",
      "Epoch: [0]  [5500/7330]  eta: 0:06:38  lr: 0.019759  loss: 0.7035 (0.7944)  loss_classifier: 0.2888 (0.3638)  loss_box_reg: 0.2262 (0.2263)  loss_objectness: 0.0762 (0.1151)  loss_rpn_box_reg: 0.0743 (0.0891)  time: 0.2136  data: 0.0060  max mem: 7663\n",
      "Epoch: [0]  [5600/7330]  eta: 0:06:17  lr: 0.019745  loss: 0.6740 (0.7925)  loss_classifier: 0.2950 (0.3626)  loss_box_reg: 0.2264 (0.2264)  loss_objectness: 0.0882 (0.1146)  loss_rpn_box_reg: 0.0592 (0.0889)  time: 0.2135  data: 0.0061  max mem: 7663\n",
      "Epoch: [0]  [5700/7330]  eta: 0:05:55  lr: 0.019731  loss: 0.6568 (0.7904)  loss_classifier: 0.2784 (0.3613)  loss_box_reg: 0.2071 (0.2262)  loss_objectness: 0.0832 (0.1141)  loss_rpn_box_reg: 0.0828 (0.0888)  time: 0.2209  data: 0.0065  max mem: 7663\n",
      "Epoch: [0]  [5800/7330]  eta: 0:05:33  lr: 0.019716  loss: 0.6521 (0.7882)  loss_classifier: 0.2834 (0.3599)  loss_box_reg: 0.2060 (0.2261)  loss_objectness: 0.0789 (0.1135)  loss_rpn_box_reg: 0.0790 (0.0887)  time: 0.2191  data: 0.0063  max mem: 7663\n",
      "Epoch: [0]  [5900/7330]  eta: 0:05:11  lr: 0.019701  loss: 0.7098 (0.7865)  loss_classifier: 0.2981 (0.3588)  loss_box_reg: 0.2280 (0.2261)  loss_objectness: 0.0822 (0.1131)  loss_rpn_box_reg: 0.0805 (0.0885)  time: 0.2206  data: 0.0061  max mem: 7663\n",
      "Epoch: [0]  [6000/7330]  eta: 0:04:49  lr: 0.019686  loss: 0.6157 (0.7843)  loss_classifier: 0.2618 (0.3575)  loss_box_reg: 0.1918 (0.2260)  loss_objectness: 0.0787 (0.1126)  loss_rpn_box_reg: 0.0638 (0.0883)  time: 0.2180  data: 0.0062  max mem: 7663\n",
      "Epoch: [0]  [6100/7330]  eta: 0:04:28  lr: 0.019670  loss: 0.6795 (0.7824)  loss_classifier: 0.2857 (0.3563)  loss_box_reg: 0.2229 (0.2259)  loss_objectness: 0.0797 (0.1120)  loss_rpn_box_reg: 0.0739 (0.0881)  time: 0.2152  data: 0.0063  max mem: 7663\n",
      "Epoch: [0]  [6200/7330]  eta: 0:04:06  lr: 0.019654  loss: 0.6620 (0.7804)  loss_classifier: 0.2829 (0.3551)  loss_box_reg: 0.2267 (0.2258)  loss_objectness: 0.0786 (0.1115)  loss_rpn_box_reg: 0.0722 (0.0880)  time: 0.2207  data: 0.0061  max mem: 7663\n",
      "Epoch: [0]  [6300/7330]  eta: 0:03:44  lr: 0.019637  loss: 0.6754 (0.7787)  loss_classifier: 0.2970 (0.3540)  loss_box_reg: 0.2241 (0.2257)  loss_objectness: 0.0779 (0.1111)  loss_rpn_box_reg: 0.0813 (0.0879)  time: 0.2184  data: 0.0063  max mem: 7663\n",
      "Epoch: [0]  [6400/7330]  eta: 0:03:22  lr: 0.019620  loss: 0.6493 (0.7769)  loss_classifier: 0.2693 (0.3530)  loss_box_reg: 0.2196 (0.2256)  loss_objectness: 0.0787 (0.1106)  loss_rpn_box_reg: 0.0763 (0.0877)  time: 0.2218  data: 0.0061  max mem: 7663\n",
      "Epoch: [0]  [6500/7330]  eta: 0:03:01  lr: 0.019603  loss: 0.6242 (0.7752)  loss_classifier: 0.2826 (0.3519)  loss_box_reg: 0.2082 (0.2255)  loss_objectness: 0.0712 (0.1102)  loss_rpn_box_reg: 0.0634 (0.0876)  time: 0.2221  data: 0.0063  max mem: 7663\n",
      "Epoch: [0]  [6600/7330]  eta: 0:02:39  lr: 0.019585  loss: 0.6036 (0.7733)  loss_classifier: 0.2596 (0.3508)  loss_box_reg: 0.2037 (0.2253)  loss_objectness: 0.0723 (0.1098)  loss_rpn_box_reg: 0.0624 (0.0874)  time: 0.2226  data: 0.0060  max mem: 7663\n",
      "Epoch: [0]  [6700/7330]  eta: 0:02:17  lr: 0.019567  loss: 0.6502 (0.7717)  loss_classifier: 0.2801 (0.3498)  loss_box_reg: 0.2261 (0.2252)  loss_objectness: 0.0791 (0.1094)  loss_rpn_box_reg: 0.0672 (0.0873)  time: 0.2236  data: 0.0062  max mem: 7663\n",
      "Epoch: [0]  [6800/7330]  eta: 0:01:55  lr: 0.019549  loss: 0.6542 (0.7699)  loss_classifier: 0.2827 (0.3487)  loss_box_reg: 0.2181 (0.2251)  loss_objectness: 0.0822 (0.1090)  loss_rpn_box_reg: 0.0749 (0.0872)  time: 0.2188  data: 0.0062  max mem: 7663\n",
      "Epoch: [0]  [6900/7330]  eta: 0:01:33  lr: 0.019530  loss: 0.6954 (0.7686)  loss_classifier: 0.2977 (0.3479)  loss_box_reg: 0.2116 (0.2250)  loss_objectness: 0.0775 (0.1086)  loss_rpn_box_reg: 0.0706 (0.0871)  time: 0.2147  data: 0.0062  max mem: 7663\n",
      "Epoch: [0]  [7000/7330]  eta: 0:01:12  lr: 0.019511  loss: 0.6202 (0.7670)  loss_classifier: 0.2637 (0.3469)  loss_box_reg: 0.2053 (0.2249)  loss_objectness: 0.0852 (0.1082)  loss_rpn_box_reg: 0.0804 (0.0870)  time: 0.2218  data: 0.0061  max mem: 7663\n",
      "Epoch: [0]  [7100/7330]  eta: 0:00:50  lr: 0.019491  loss: 0.6596 (0.7655)  loss_classifier: 0.2824 (0.3461)  loss_box_reg: 0.2166 (0.2248)  loss_objectness: 0.0820 (0.1078)  loss_rpn_box_reg: 0.0967 (0.0868)  time: 0.2154  data: 0.0061  max mem: 7663\n",
      "Epoch: [0]  [7200/7330]  eta: 0:00:28  lr: 0.019471  loss: 0.6087 (0.7636)  loss_classifier: 0.2702 (0.3450)  loss_box_reg: 0.2086 (0.2246)  loss_objectness: 0.0644 (0.1073)  loss_rpn_box_reg: 0.0728 (0.0866)  time: 0.2193  data: 0.0062  max mem: 7663\n",
      "Epoch: [0]  [7300/7330]  eta: 0:00:06  lr: 0.019451  loss: 0.5805 (0.7619)  loss_classifier: 0.2481 (0.3440)  loss_box_reg: 0.1949 (0.2245)  loss_objectness: 0.0719 (0.1069)  loss_rpn_box_reg: 0.0542 (0.0864)  time: 0.2205  data: 0.0068  max mem: 7663\n",
      "Epoch: [0]  [7329/7330]  eta: 0:00:00  lr: 0.019445  loss: 0.6491 (0.7614)  loss_classifier: 0.2689 (0.3437)  loss_box_reg: 0.2167 (0.2245)  loss_objectness: 0.0774 (0.1068)  loss_rpn_box_reg: 0.0620 (0.0864)  time: 0.2071  data: 0.0064  max mem: 7663\n",
      "Epoch: [0] Total time: 0:26:39 (0.2182 s / it)\n",
      "Test:  [  0/313]  eta: 0:02:29  model_time: 0.1583 (0.1583)  evaluator_time: 0.0224 (0.0224)  time: 0.4772  data: 0.2883  max mem: 8150\n",
      "Test:  [312/313]  eta: 0:00:00  model_time: 0.1282 (0.1294)  evaluator_time: 0.0157 (0.0221)  time: 0.1918  data: 0.0059  max mem: 8983\n",
      "Test: Total time: 0:00:51 (0.1650 s / it)\n",
      "Averaged stats: model_time: 0.1282 (0.1294)  evaluator_time: 0.0157 (0.0221)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.328\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.611\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.261\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.094\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.386\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.547\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.262\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.321\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.333\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.104\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.386\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.547\n",
      "Epoch: [1]  [   0/7330]  eta: 1:36:05  lr: 0.019444  loss: 0.8109 (0.8109)  loss_classifier: 0.3653 (0.3653)  loss_box_reg: 0.2134 (0.2134)  loss_objectness: 0.0990 (0.0990)  loss_rpn_box_reg: 0.1331 (0.1331)  time: 0.7865  data: 0.5893  max mem: 8983\n",
      "Epoch: [1]  [ 100/7330]  eta: 0:27:12  lr: 0.019424  loss: 0.6333 (0.6683)  loss_classifier: 0.2706 (0.2866)  loss_box_reg: 0.2178 (0.2233)  loss_objectness: 0.0725 (0.0809)  loss_rpn_box_reg: 0.0726 (0.0775)  time: 0.2245  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [ 200/7330]  eta: 0:26:23  lr: 0.019402  loss: 0.6181 (0.6559)  loss_classifier: 0.2586 (0.2799)  loss_box_reg: 0.1996 (0.2175)  loss_objectness: 0.0709 (0.0806)  loss_rpn_box_reg: 0.0744 (0.0779)  time: 0.2185  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [ 300/7330]  eta: 0:25:51  lr: 0.019381  loss: 0.6230 (0.6538)  loss_classifier: 0.2667 (0.2780)  loss_box_reg: 0.2102 (0.2175)  loss_objectness: 0.0693 (0.0807)  loss_rpn_box_reg: 0.0664 (0.0776)  time: 0.2212  data: 0.0066  max mem: 8983\n",
      "Epoch: [1]  [ 400/7330]  eta: 0:25:27  lr: 0.019359  loss: 0.7007 (0.6557)  loss_classifier: 0.2974 (0.2786)  loss_box_reg: 0.2111 (0.2178)  loss_objectness: 0.0831 (0.0807)  loss_rpn_box_reg: 0.0759 (0.0786)  time: 0.2200  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [ 500/7330]  eta: 0:25:05  lr: 0.019337  loss: 0.6542 (0.6534)  loss_classifier: 0.2809 (0.2781)  loss_box_reg: 0.2122 (0.2170)  loss_objectness: 0.0821 (0.0801)  loss_rpn_box_reg: 0.0796 (0.0783)  time: 0.2222  data: 0.0065  max mem: 8983\n",
      "Epoch: [1]  [ 600/7330]  eta: 0:24:41  lr: 0.019314  loss: 0.6563 (0.6528)  loss_classifier: 0.2713 (0.2775)  loss_box_reg: 0.2119 (0.2164)  loss_objectness: 0.0889 (0.0806)  loss_rpn_box_reg: 0.0680 (0.0782)  time: 0.2190  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [ 700/7330]  eta: 0:24:19  lr: 0.019291  loss: 0.5873 (0.6502)  loss_classifier: 0.2488 (0.2765)  loss_box_reg: 0.1923 (0.2161)  loss_objectness: 0.0710 (0.0800)  loss_rpn_box_reg: 0.0684 (0.0776)  time: 0.2202  data: 0.0062  max mem: 8983\n",
      "Epoch: [1]  [ 800/7330]  eta: 0:23:58  lr: 0.019267  loss: 0.5927 (0.6484)  loss_classifier: 0.2599 (0.2757)  loss_box_reg: 0.1910 (0.2156)  loss_objectness: 0.0690 (0.0798)  loss_rpn_box_reg: 0.0673 (0.0772)  time: 0.2184  data: 0.0063  max mem: 8983\n",
      "Epoch: [1]  [ 900/7330]  eta: 0:23:33  lr: 0.019244  loss: 0.6669 (0.6484)  loss_classifier: 0.2708 (0.2753)  loss_box_reg: 0.2112 (0.2157)  loss_objectness: 0.0789 (0.0801)  loss_rpn_box_reg: 0.0789 (0.0774)  time: 0.2184  data: 0.0063  max mem: 8983\n",
      "Epoch: [1]  [1000/7330]  eta: 0:23:10  lr: 0.019219  loss: 0.5750 (0.6460)  loss_classifier: 0.2462 (0.2741)  loss_box_reg: 0.1894 (0.2151)  loss_objectness: 0.0705 (0.0798)  loss_rpn_box_reg: 0.0690 (0.0770)  time: 0.2183  data: 0.0066  max mem: 8983\n",
      "Epoch: [1]  [1100/7330]  eta: 0:22:48  lr: 0.019195  loss: 0.6302 (0.6469)  loss_classifier: 0.2673 (0.2745)  loss_box_reg: 0.2156 (0.2156)  loss_objectness: 0.0698 (0.0797)  loss_rpn_box_reg: 0.0775 (0.0771)  time: 0.2215  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [1200/7330]  eta: 0:22:26  lr: 0.019170  loss: 0.6728 (0.6474)  loss_classifier: 0.2781 (0.2750)  loss_box_reg: 0.2280 (0.2160)  loss_objectness: 0.0765 (0.0794)  loss_rpn_box_reg: 0.0754 (0.0770)  time: 0.2176  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [1300/7330]  eta: 0:22:05  lr: 0.019145  loss: 0.6489 (0.6474)  loss_classifier: 0.2756 (0.2750)  loss_box_reg: 0.2172 (0.2160)  loss_objectness: 0.0786 (0.0793)  loss_rpn_box_reg: 0.0750 (0.0772)  time: 0.2211  data: 0.0062  max mem: 8983\n",
      "Epoch: [1]  [1400/7330]  eta: 0:21:43  lr: 0.019119  loss: 0.5756 (0.6461)  loss_classifier: 0.2593 (0.2746)  loss_box_reg: 0.1906 (0.2156)  loss_objectness: 0.0648 (0.0789)  loss_rpn_box_reg: 0.0713 (0.0770)  time: 0.2205  data: 0.0061  max mem: 8983\n",
      "Epoch: [1]  [1500/7330]  eta: 0:21:21  lr: 0.019093  loss: 0.6233 (0.6448)  loss_classifier: 0.2500 (0.2741)  loss_box_reg: 0.2160 (0.2152)  loss_objectness: 0.0789 (0.0788)  loss_rpn_box_reg: 0.0753 (0.0767)  time: 0.2249  data: 0.0067  max mem: 8983\n",
      "Epoch: [1]  [1600/7330]  eta: 0:20:58  lr: 0.019067  loss: 0.6172 (0.6434)  loss_classifier: 0.2603 (0.2735)  loss_box_reg: 0.2011 (0.2149)  loss_objectness: 0.0738 (0.0787)  loss_rpn_box_reg: 0.0650 (0.0763)  time: 0.2187  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [1700/7330]  eta: 0:20:36  lr: 0.019040  loss: 0.6324 (0.6431)  loss_classifier: 0.2667 (0.2735)  loss_box_reg: 0.2050 (0.2147)  loss_objectness: 0.0759 (0.0787)  loss_rpn_box_reg: 0.0741 (0.0763)  time: 0.2218  data: 0.0061  max mem: 8983\n",
      "Epoch: [1]  [1800/7330]  eta: 0:20:15  lr: 0.019013  loss: 0.6798 (0.6419)  loss_classifier: 0.2752 (0.2730)  loss_box_reg: 0.2183 (0.2144)  loss_objectness: 0.0756 (0.0784)  loss_rpn_box_reg: 0.0720 (0.0761)  time: 0.2192  data: 0.0063  max mem: 8983\n",
      "Epoch: [1]  [1900/7330]  eta: 0:19:53  lr: 0.018986  loss: 0.6149 (0.6420)  loss_classifier: 0.2727 (0.2731)  loss_box_reg: 0.2139 (0.2145)  loss_objectness: 0.0664 (0.0783)  loss_rpn_box_reg: 0.0651 (0.0760)  time: 0.2194  data: 0.0066  max mem: 8983\n",
      "Epoch: [1]  [2000/7330]  eta: 0:19:31  lr: 0.018958  loss: 0.6512 (0.6423)  loss_classifier: 0.2768 (0.2732)  loss_box_reg: 0.2117 (0.2149)  loss_objectness: 0.0780 (0.0783)  loss_rpn_box_reg: 0.0702 (0.0761)  time: 0.2199  data: 0.0062  max mem: 8983\n",
      "Epoch: [1]  [2100/7330]  eta: 0:19:08  lr: 0.018930  loss: 0.5832 (0.6412)  loss_classifier: 0.2584 (0.2728)  loss_box_reg: 0.1891 (0.2145)  loss_objectness: 0.0715 (0.0781)  loss_rpn_box_reg: 0.0702 (0.0759)  time: 0.2176  data: 0.0062  max mem: 8983\n",
      "Epoch: [1]  [2200/7330]  eta: 0:18:46  lr: 0.018902  loss: 0.6076 (0.6402)  loss_classifier: 0.2459 (0.2723)  loss_box_reg: 0.1838 (0.2142)  loss_objectness: 0.0683 (0.0779)  loss_rpn_box_reg: 0.0677 (0.0759)  time: 0.2147  data: 0.0065  max mem: 8983\n",
      "Epoch: [1]  [2300/7330]  eta: 0:18:23  lr: 0.018873  loss: 0.6653 (0.6406)  loss_classifier: 0.2817 (0.2725)  loss_box_reg: 0.2164 (0.2143)  loss_objectness: 0.0752 (0.0779)  loss_rpn_box_reg: 0.0672 (0.0759)  time: 0.2223  data: 0.0068  max mem: 8983\n",
      "Epoch: [1]  [2400/7330]  eta: 0:18:01  lr: 0.018844  loss: 0.6036 (0.6397)  loss_classifier: 0.2579 (0.2721)  loss_box_reg: 0.2173 (0.2143)  loss_objectness: 0.0726 (0.0776)  loss_rpn_box_reg: 0.0602 (0.0757)  time: 0.2201  data: 0.0063  max mem: 8983\n",
      "Epoch: [1]  [2500/7330]  eta: 0:17:39  lr: 0.018814  loss: 0.5640 (0.6387)  loss_classifier: 0.2342 (0.2717)  loss_box_reg: 0.1778 (0.2140)  loss_objectness: 0.0657 (0.0774)  loss_rpn_box_reg: 0.0646 (0.0756)  time: 0.2184  data: 0.0062  max mem: 8983\n",
      "Epoch: [1]  [2600/7330]  eta: 0:17:17  lr: 0.018784  loss: 0.6543 (0.6383)  loss_classifier: 0.2702 (0.2716)  loss_box_reg: 0.2263 (0.2139)  loss_objectness: 0.0712 (0.0773)  loss_rpn_box_reg: 0.0699 (0.0754)  time: 0.2178  data: 0.0066  max mem: 8983\n",
      "Epoch: [1]  [2700/7330]  eta: 0:16:56  lr: 0.018754  loss: 0.5824 (0.6375)  loss_classifier: 0.2364 (0.2712)  loss_box_reg: 0.1874 (0.2137)  loss_objectness: 0.0748 (0.0771)  loss_rpn_box_reg: 0.0661 (0.0755)  time: 0.2248  data: 0.0069  max mem: 8983\n",
      "Epoch: [1]  [2800/7330]  eta: 0:16:34  lr: 0.018723  loss: 0.6016 (0.6369)  loss_classifier: 0.2492 (0.2710)  loss_box_reg: 0.1982 (0.2136)  loss_objectness: 0.0680 (0.0770)  loss_rpn_box_reg: 0.0692 (0.0753)  time: 0.2188  data: 0.0065  max mem: 8983\n",
      "Epoch: [1]  [2900/7330]  eta: 0:16:12  lr: 0.018693  loss: 0.6659 (0.6369)  loss_classifier: 0.2596 (0.2708)  loss_box_reg: 0.2036 (0.2135)  loss_objectness: 0.0786 (0.0770)  loss_rpn_box_reg: 0.0837 (0.0756)  time: 0.2180  data: 0.0066  max mem: 8983\n",
      "Epoch: [1]  [3000/7330]  eta: 0:15:50  lr: 0.018661  loss: 0.6331 (0.6362)  loss_classifier: 0.2668 (0.2705)  loss_box_reg: 0.2132 (0.2133)  loss_objectness: 0.0684 (0.0768)  loss_rpn_box_reg: 0.0772 (0.0755)  time: 0.2193  data: 0.0063  max mem: 8983\n",
      "Epoch: [1]  [3100/7330]  eta: 0:15:28  lr: 0.018630  loss: 0.6084 (0.6359)  loss_classifier: 0.2641 (0.2705)  loss_box_reg: 0.2011 (0.2132)  loss_objectness: 0.0807 (0.0768)  loss_rpn_box_reg: 0.0578 (0.0753)  time: 0.2096  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [3200/7330]  eta: 0:15:06  lr: 0.018598  loss: 0.6424 (0.6356)  loss_classifier: 0.2790 (0.2703)  loss_box_reg: 0.2241 (0.2133)  loss_objectness: 0.0670 (0.0767)  loss_rpn_box_reg: 0.0639 (0.0753)  time: 0.2201  data: 0.0062  max mem: 8983\n",
      "Epoch: [1]  [3300/7330]  eta: 0:14:44  lr: 0.018566  loss: 0.5883 (0.6357)  loss_classifier: 0.2513 (0.2704)  loss_box_reg: 0.2171 (0.2134)  loss_objectness: 0.0682 (0.0766)  loss_rpn_box_reg: 0.0682 (0.0753)  time: 0.2169  data: 0.0061  max mem: 8983\n",
      "Epoch: [1]  [3400/7330]  eta: 0:14:22  lr: 0.018533  loss: 0.6430 (0.6358)  loss_classifier: 0.2612 (0.2704)  loss_box_reg: 0.2143 (0.2134)  loss_objectness: 0.0719 (0.0766)  loss_rpn_box_reg: 0.0778 (0.0754)  time: 0.2152  data: 0.0062  max mem: 8983\n",
      "Epoch: [1]  [3500/7330]  eta: 0:14:00  lr: 0.018500  loss: 0.5939 (0.6354)  loss_classifier: 0.2481 (0.2702)  loss_box_reg: 0.2011 (0.2134)  loss_objectness: 0.0687 (0.0765)  loss_rpn_box_reg: 0.0616 (0.0754)  time: 0.2209  data: 0.0069  max mem: 8983\n",
      "Epoch: [1]  [3600/7330]  eta: 0:13:38  lr: 0.018467  loss: 0.5709 (0.6353)  loss_classifier: 0.2486 (0.2702)  loss_box_reg: 0.1925 (0.2133)  loss_objectness: 0.0687 (0.0764)  loss_rpn_box_reg: 0.0630 (0.0754)  time: 0.2174  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [3700/7330]  eta: 0:13:16  lr: 0.018433  loss: 0.5993 (0.6351)  loss_classifier: 0.2667 (0.2700)  loss_box_reg: 0.2069 (0.2132)  loss_objectness: 0.0653 (0.0764)  loss_rpn_box_reg: 0.0714 (0.0755)  time: 0.2183  data: 0.0063  max mem: 8983\n",
      "Epoch: [1]  [3800/7330]  eta: 0:12:54  lr: 0.018399  loss: 0.6117 (0.6349)  loss_classifier: 0.2748 (0.2700)  loss_box_reg: 0.1941 (0.2131)  loss_objectness: 0.0702 (0.0764)  loss_rpn_box_reg: 0.0687 (0.0754)  time: 0.2219  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [3900/7330]  eta: 0:12:32  lr: 0.018365  loss: 0.5298 (0.6350)  loss_classifier: 0.2273 (0.2699)  loss_box_reg: 0.1978 (0.2132)  loss_objectness: 0.0658 (0.0764)  loss_rpn_box_reg: 0.0769 (0.0755)  time: 0.2203  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [4000/7330]  eta: 0:12:10  lr: 0.018330  loss: 0.6548 (0.6347)  loss_classifier: 0.2785 (0.2698)  loss_box_reg: 0.2180 (0.2132)  loss_objectness: 0.0796 (0.0763)  loss_rpn_box_reg: 0.0719 (0.0755)  time: 0.2222  data: 0.0066  max mem: 8983\n",
      "Epoch: [1]  [4100/7330]  eta: 0:11:48  lr: 0.018296  loss: 0.5844 (0.6342)  loss_classifier: 0.2604 (0.2695)  loss_box_reg: 0.1993 (0.2130)  loss_objectness: 0.0659 (0.0762)  loss_rpn_box_reg: 0.0568 (0.0754)  time: 0.2056  data: 0.0066  max mem: 8983\n",
      "Epoch: [1]  [4200/7330]  eta: 0:11:26  lr: 0.018260  loss: 0.6096 (0.6337)  loss_classifier: 0.2655 (0.2694)  loss_box_reg: 0.1975 (0.2128)  loss_objectness: 0.0722 (0.0762)  loss_rpn_box_reg: 0.0685 (0.0753)  time: 0.2242  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [4300/7330]  eta: 0:11:04  lr: 0.018225  loss: 0.5770 (0.6334)  loss_classifier: 0.2547 (0.2693)  loss_box_reg: 0.1910 (0.2126)  loss_objectness: 0.0660 (0.0761)  loss_rpn_box_reg: 0.0781 (0.0754)  time: 0.2212  data: 0.0062  max mem: 8983\n",
      "Epoch: [1]  [4400/7330]  eta: 0:10:42  lr: 0.018189  loss: 0.6214 (0.6331)  loss_classifier: 0.2516 (0.2691)  loss_box_reg: 0.1999 (0.2125)  loss_objectness: 0.0752 (0.0760)  loss_rpn_box_reg: 0.0628 (0.0754)  time: 0.2148  data: 0.0061  max mem: 8983\n",
      "Epoch: [1]  [4500/7330]  eta: 0:10:20  lr: 0.018153  loss: 0.6221 (0.6329)  loss_classifier: 0.2732 (0.2690)  loss_box_reg: 0.2134 (0.2125)  loss_objectness: 0.0645 (0.0759)  loss_rpn_box_reg: 0.0634 (0.0754)  time: 0.2195  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [4600/7330]  eta: 0:09:58  lr: 0.018116  loss: 0.6411 (0.6321)  loss_classifier: 0.2609 (0.2687)  loss_box_reg: 0.2095 (0.2124)  loss_objectness: 0.0721 (0.0758)  loss_rpn_box_reg: 0.0688 (0.0753)  time: 0.2213  data: 0.0066  max mem: 8983\n",
      "Epoch: [1]  [4700/7330]  eta: 0:09:36  lr: 0.018079  loss: 0.6209 (0.6319)  loss_classifier: 0.2622 (0.2685)  loss_box_reg: 0.2085 (0.2124)  loss_objectness: 0.0709 (0.0758)  loss_rpn_box_reg: 0.0650 (0.0752)  time: 0.2108  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [4800/7330]  eta: 0:09:14  lr: 0.018042  loss: 0.6110 (0.6313)  loss_classifier: 0.2552 (0.2683)  loss_box_reg: 0.2057 (0.2121)  loss_objectness: 0.0737 (0.0757)  loss_rpn_box_reg: 0.0762 (0.0752)  time: 0.2164  data: 0.0063  max mem: 8983\n",
      "Epoch: [1]  [4900/7330]  eta: 0:08:52  lr: 0.018004  loss: 0.5896 (0.6309)  loss_classifier: 0.2596 (0.2681)  loss_box_reg: 0.1973 (0.2120)  loss_objectness: 0.0724 (0.0757)  loss_rpn_box_reg: 0.0775 (0.0751)  time: 0.2191  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [5000/7330]  eta: 0:08:31  lr: 0.017967  loss: 0.6197 (0.6309)  loss_classifier: 0.2670 (0.2681)  loss_box_reg: 0.2096 (0.2121)  loss_objectness: 0.0714 (0.0757)  loss_rpn_box_reg: 0.0794 (0.0751)  time: 0.2215  data: 0.0067  max mem: 8983\n",
      "Epoch: [1]  [5100/7330]  eta: 0:08:09  lr: 0.017929  loss: 0.6069 (0.6304)  loss_classifier: 0.2510 (0.2678)  loss_box_reg: 0.2112 (0.2120)  loss_objectness: 0.0693 (0.0756)  loss_rpn_box_reg: 0.0755 (0.0750)  time: 0.2217  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [5200/7330]  eta: 0:07:47  lr: 0.017890  loss: 0.6448 (0.6303)  loss_classifier: 0.2632 (0.2677)  loss_box_reg: 0.2058 (0.2120)  loss_objectness: 0.0821 (0.0755)  loss_rpn_box_reg: 0.0693 (0.0751)  time: 0.2176  data: 0.0063  max mem: 8983\n",
      "Epoch: [1]  [5300/7330]  eta: 0:07:25  lr: 0.017851  loss: 0.6025 (0.6301)  loss_classifier: 0.2521 (0.2676)  loss_box_reg: 0.1956 (0.2119)  loss_objectness: 0.0684 (0.0755)  loss_rpn_box_reg: 0.0742 (0.0751)  time: 0.2197  data: 0.0061  max mem: 8983\n",
      "Epoch: [1]  [5400/7330]  eta: 0:07:03  lr: 0.017812  loss: 0.6154 (0.6298)  loss_classifier: 0.2472 (0.2675)  loss_box_reg: 0.1940 (0.2118)  loss_objectness: 0.0699 (0.0755)  loss_rpn_box_reg: 0.0744 (0.0751)  time: 0.2214  data: 0.0067  max mem: 8983\n",
      "Epoch: [1]  [5500/7330]  eta: 0:06:41  lr: 0.017773  loss: 0.5678 (0.6296)  loss_classifier: 0.2495 (0.2674)  loss_box_reg: 0.2033 (0.2117)  loss_objectness: 0.0614 (0.0755)  loss_rpn_box_reg: 0.0755 (0.0751)  time: 0.2210  data: 0.0063  max mem: 8983\n",
      "Epoch: [1]  [5600/7330]  eta: 0:06:19  lr: 0.017733  loss: 0.5773 (0.6292)  loss_classifier: 0.2374 (0.2671)  loss_box_reg: 0.2002 (0.2116)  loss_objectness: 0.0660 (0.0754)  loss_rpn_box_reg: 0.0610 (0.0751)  time: 0.2168  data: 0.0062  max mem: 8983\n",
      "Epoch: [1]  [5700/7330]  eta: 0:05:57  lr: 0.017693  loss: 0.6070 (0.6292)  loss_classifier: 0.2485 (0.2671)  loss_box_reg: 0.2028 (0.2116)  loss_objectness: 0.0642 (0.0754)  loss_rpn_box_reg: 0.0710 (0.0751)  time: 0.2166  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [5800/7330]  eta: 0:05:35  lr: 0.017653  loss: 0.5816 (0.6287)  loss_classifier: 0.2466 (0.2669)  loss_box_reg: 0.1878 (0.2114)  loss_objectness: 0.0684 (0.0752)  loss_rpn_box_reg: 0.0662 (0.0751)  time: 0.2197  data: 0.0061  max mem: 8983\n",
      "Epoch: [1]  [5900/7330]  eta: 0:05:13  lr: 0.017612  loss: 0.6411 (0.6284)  loss_classifier: 0.2697 (0.2668)  loss_box_reg: 0.2027 (0.2113)  loss_objectness: 0.0657 (0.0752)  loss_rpn_box_reg: 0.0751 (0.0752)  time: 0.2163  data: 0.0065  max mem: 8983\n",
      "Epoch: [1]  [6000/7330]  eta: 0:04:51  lr: 0.017571  loss: 0.6575 (0.6285)  loss_classifier: 0.2744 (0.2668)  loss_box_reg: 0.2242 (0.2113)  loss_objectness: 0.0766 (0.0752)  loss_rpn_box_reg: 0.0771 (0.0752)  time: 0.2182  data: 0.0063  max mem: 8983\n",
      "Epoch: [1]  [6100/7330]  eta: 0:04:29  lr: 0.017530  loss: 0.6063 (0.6284)  loss_classifier: 0.2527 (0.2667)  loss_box_reg: 0.1972 (0.2113)  loss_objectness: 0.0686 (0.0751)  loss_rpn_box_reg: 0.0847 (0.0752)  time: 0.2220  data: 0.0063  max mem: 8983\n",
      "Epoch: [1]  [6200/7330]  eta: 0:04:07  lr: 0.017489  loss: 0.5847 (0.6280)  loss_classifier: 0.2432 (0.2665)  loss_box_reg: 0.1992 (0.2112)  loss_objectness: 0.0694 (0.0751)  loss_rpn_box_reg: 0.0687 (0.0753)  time: 0.2152  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [6300/7330]  eta: 0:03:45  lr: 0.017447  loss: 0.6027 (0.6276)  loss_classifier: 0.2631 (0.2663)  loss_box_reg: 0.1952 (0.2111)  loss_objectness: 0.0704 (0.0751)  loss_rpn_box_reg: 0.0787 (0.0752)  time: 0.2126  data: 0.0064  max mem: 8983\n",
      "Epoch: [1]  [6400/7330]  eta: 0:03:23  lr: 0.017405  loss: 0.6158 (0.6273)  loss_classifier: 0.2660 (0.2662)  loss_box_reg: 0.2086 (0.2110)  loss_objectness: 0.0684 (0.0750)  loss_rpn_box_reg: 0.0624 (0.0751)  time: 0.2228  data: 0.0065  max mem: 8983\n",
      "Epoch: [1]  [6500/7330]  eta: 0:03:01  lr: 0.017363  loss: 0.5951 (0.6269)  loss_classifier: 0.2604 (0.2660)  loss_box_reg: 0.1962 (0.2109)  loss_objectness: 0.0660 (0.0749)  loss_rpn_box_reg: 0.0739 (0.0750)  time: 0.2210  data: 0.0062  max mem: 8983\n",
      "Epoch: [1]  [6600/7330]  eta: 0:02:40  lr: 0.017320  loss: 0.5798 (0.6265)  loss_classifier: 0.2450 (0.2658)  loss_box_reg: 0.1942 (0.2107)  loss_objectness: 0.0598 (0.0749)  loss_rpn_box_reg: 0.0701 (0.0750)  time: 0.2248  data: 0.0061  max mem: 8983\n",
      "Epoch: [1]  [6700/7330]  eta: 0:02:18  lr: 0.017277  loss: 0.6111 (0.6262)  loss_classifier: 0.2570 (0.2657)  loss_box_reg: 0.2095 (0.2107)  loss_objectness: 0.0677 (0.0748)  loss_rpn_box_reg: 0.0659 (0.0750)  time: 0.2178  data: 0.0063  max mem: 8983\n",
      "Epoch: [1]  [6800/7330]  eta: 0:01:56  lr: 0.017234  loss: 0.6638 (0.6260)  loss_classifier: 0.2846 (0.2657)  loss_box_reg: 0.2139 (0.2106)  loss_objectness: 0.0704 (0.0747)  loss_rpn_box_reg: 0.0728 (0.0749)  time: 0.2204  data: 0.0066  max mem: 8983\n",
      "Epoch: [1]  [6900/7330]  eta: 0:01:34  lr: 0.017190  loss: 0.6113 (0.6257)  loss_classifier: 0.2608 (0.2655)  loss_box_reg: 0.1969 (0.2105)  loss_objectness: 0.0642 (0.0747)  loss_rpn_box_reg: 0.0762 (0.0749)  time: 0.2203  data: 0.0063  max mem: 8983\n",
      "Epoch: [1]  [7000/7330]  eta: 0:01:12  lr: 0.017146  loss: 0.5941 (0.6256)  loss_classifier: 0.2434 (0.2655)  loss_box_reg: 0.1920 (0.2105)  loss_objectness: 0.0721 (0.0746)  loss_rpn_box_reg: 0.0722 (0.0749)  time: 0.2151  data: 0.0061  max mem: 8983\n",
      "Epoch: [1]  [7100/7330]  eta: 0:00:50  lr: 0.017102  loss: 0.5774 (0.6256)  loss_classifier: 0.2465 (0.2655)  loss_box_reg: 0.1936 (0.2105)  loss_objectness: 0.0650 (0.0746)  loss_rpn_box_reg: 0.0684 (0.0750)  time: 0.2228  data: 0.0063  max mem: 8983\n",
      "Epoch: [1]  [7200/7330]  eta: 0:00:28  lr: 0.017058  loss: 0.5263 (0.6253)  loss_classifier: 0.2315 (0.2654)  loss_box_reg: 0.1841 (0.2105)  loss_objectness: 0.0557 (0.0745)  loss_rpn_box_reg: 0.0619 (0.0749)  time: 0.2184  data: 0.0062  max mem: 8983\n",
      "Epoch: [1]  [7300/7330]  eta: 0:00:06  lr: 0.017013  loss: 0.5683 (0.6250)  loss_classifier: 0.2415 (0.2653)  loss_box_reg: 0.1931 (0.2105)  loss_objectness: 0.0579 (0.0745)  loss_rpn_box_reg: 0.0629 (0.0748)  time: 0.2226  data: 0.0062  max mem: 8983\n",
      "Epoch: [1]  [7329/7330]  eta: 0:00:00  lr: 0.017000  loss: 0.5949 (0.6249)  loss_classifier: 0.2484 (0.2653)  loss_box_reg: 0.2055 (0.2104)  loss_objectness: 0.0727 (0.0745)  loss_rpn_box_reg: 0.0671 (0.0748)  time: 0.2098  data: 0.0063  max mem: 8983\n",
      "Epoch: [1] Total time: 0:26:47 (0.2193 s / it)\n",
      "Test:  [  0/313]  eta: 0:02:30  model_time: 0.1369 (0.1369)  evaluator_time: 0.0255 (0.0255)  time: 0.4800  data: 0.3082  max mem: 8983\n",
      "Test:  [312/313]  eta: 0:00:00  model_time: 0.1286 (0.1299)  evaluator_time: 0.0166 (0.0222)  time: 0.1933  data: 0.0062  max mem: 8983\n",
      "Test: Total time: 0:00:52 (0.1662 s / it)\n",
      "Averaged stats: model_time: 0.1286 (0.1299)  evaluator_time: 0.0166 (0.0222)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.357\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.643\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.385\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.222\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.315\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.563\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.322\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.370\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.376\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.226\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.326\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.567\n",
      "Epoch: [2]  [   0/7330]  eta: 1:46:45  lr: 0.017000  loss: 0.5313 (0.5313)  loss_classifier: 0.2050 (0.2050)  loss_box_reg: 0.1881 (0.1881)  loss_objectness: 0.0516 (0.0516)  loss_rpn_box_reg: 0.0867 (0.0867)  time: 0.8739  data: 0.6371  max mem: 8983\n",
      "Epoch: [2]  [ 100/7330]  eta: 0:27:18  lr: 0.016955  loss: 0.5697 (0.5936)  loss_classifier: 0.2471 (0.2508)  loss_box_reg: 0.2110 (0.2031)  loss_objectness: 0.0655 (0.0683)  loss_rpn_box_reg: 0.0735 (0.0715)  time: 0.2154  data: 0.0064  max mem: 8983\n",
      "Epoch: [2]  [ 200/7330]  eta: 0:26:19  lr: 0.016909  loss: 0.5898 (0.5911)  loss_classifier: 0.2441 (0.2502)  loss_box_reg: 0.2056 (0.2015)  loss_objectness: 0.0684 (0.0687)  loss_rpn_box_reg: 0.0637 (0.0707)  time: 0.2123  data: 0.0067  max mem: 8983\n",
      "Epoch: [2]  [ 300/7330]  eta: 0:25:57  lr: 0.016864  loss: 0.5379 (0.5901)  loss_classifier: 0.2223 (0.2495)  loss_box_reg: 0.1942 (0.2016)  loss_objectness: 0.0606 (0.0681)  loss_rpn_box_reg: 0.0657 (0.0709)  time: 0.2210  data: 0.0065  max mem: 8983\n",
      "Epoch: [2]  [ 400/7330]  eta: 0:25:31  lr: 0.016818  loss: 0.5781 (0.5900)  loss_classifier: 0.2406 (0.2492)  loss_box_reg: 0.2058 (0.2016)  loss_objectness: 0.0628 (0.0687)  loss_rpn_box_reg: 0.0691 (0.0705)  time: 0.2148  data: 0.0066  max mem: 8983\n",
      "Epoch: [2]  [ 500/7330]  eta: 0:25:05  lr: 0.016772  loss: 0.5506 (0.5906)  loss_classifier: 0.2387 (0.2493)  loss_box_reg: 0.1841 (0.2019)  loss_objectness: 0.0581 (0.0685)  loss_rpn_box_reg: 0.0606 (0.0708)  time: 0.2172  data: 0.0062  max mem: 8983\n",
      "Epoch: [2]  [ 600/7330]  eta: 0:24:39  lr: 0.016726  loss: 0.5803 (0.5898)  loss_classifier: 0.2378 (0.2486)  loss_box_reg: 0.1987 (0.2011)  loss_objectness: 0.0639 (0.0687)  loss_rpn_box_reg: 0.0666 (0.0714)  time: 0.2180  data: 0.0065  max mem: 8983\n",
      "Epoch: [2]  [ 700/7330]  eta: 0:24:16  lr: 0.016679  loss: 0.5616 (0.5904)  loss_classifier: 0.2323 (0.2487)  loss_box_reg: 0.1956 (0.2010)  loss_objectness: 0.0621 (0.0690)  loss_rpn_box_reg: 0.0635 (0.0717)  time: 0.2228  data: 0.0064  max mem: 8983\n",
      "Epoch: [2]  [ 800/7330]  eta: 0:23:56  lr: 0.016632  loss: 0.5954 (0.5914)  loss_classifier: 0.2552 (0.2489)  loss_box_reg: 0.2105 (0.2018)  loss_objectness: 0.0674 (0.0688)  loss_rpn_box_reg: 0.0745 (0.0719)  time: 0.2288  data: 0.0067  max mem: 8983\n",
      "Epoch: [2]  [ 900/7330]  eta: 0:23:34  lr: 0.016585  loss: 0.5652 (0.5901)  loss_classifier: 0.2520 (0.2483)  loss_box_reg: 0.2012 (0.2016)  loss_objectness: 0.0619 (0.0684)  loss_rpn_box_reg: 0.0652 (0.0718)  time: 0.2210  data: 0.0062  max mem: 8983\n",
      "Epoch: [2]  [1000/7330]  eta: 0:23:12  lr: 0.016538  loss: 0.5725 (0.5893)  loss_classifier: 0.2384 (0.2478)  loss_box_reg: 0.1942 (0.2015)  loss_objectness: 0.0641 (0.0683)  loss_rpn_box_reg: 0.0658 (0.0718)  time: 0.2213  data: 0.0062  max mem: 8983\n",
      "Epoch: [2]  [1100/7330]  eta: 0:22:51  lr: 0.016490  loss: 0.5723 (0.5902)  loss_classifier: 0.2411 (0.2485)  loss_box_reg: 0.1890 (0.2018)  loss_objectness: 0.0653 (0.0683)  loss_rpn_box_reg: 0.0694 (0.0716)  time: 0.2144  data: 0.0064  max mem: 8983\n",
      "Epoch: [2]  [1200/7330]  eta: 0:22:28  lr: 0.016442  loss: 0.5665 (0.5911)  loss_classifier: 0.2388 (0.2490)  loss_box_reg: 0.1896 (0.2019)  loss_objectness: 0.0678 (0.0686)  loss_rpn_box_reg: 0.0664 (0.0716)  time: 0.2185  data: 0.0064  max mem: 8983\n",
      "Epoch: [2]  [1300/7330]  eta: 0:22:05  lr: 0.016394  loss: 0.6098 (0.5931)  loss_classifier: 0.2543 (0.2499)  loss_box_reg: 0.2067 (0.2026)  loss_objectness: 0.0709 (0.0689)  loss_rpn_box_reg: 0.0713 (0.0717)  time: 0.2159  data: 0.0064  max mem: 8983\n",
      "Epoch: [2]  [1400/7330]  eta: 0:21:43  lr: 0.016345  loss: 0.5908 (0.5925)  loss_classifier: 0.2403 (0.2496)  loss_box_reg: 0.2019 (0.2028)  loss_objectness: 0.0635 (0.0687)  loss_rpn_box_reg: 0.0575 (0.0713)  time: 0.2212  data: 0.0063  max mem: 8983\n",
      "Epoch: [2]  [1500/7330]  eta: 0:21:20  lr: 0.016297  loss: 0.5715 (0.5925)  loss_classifier: 0.2350 (0.2495)  loss_box_reg: 0.2030 (0.2028)  loss_objectness: 0.0713 (0.0688)  loss_rpn_box_reg: 0.0704 (0.0715)  time: 0.2247  data: 0.0063  max mem: 8983\n",
      "Epoch: [2]  [1600/7330]  eta: 0:20:58  lr: 0.016248  loss: 0.5967 (0.5931)  loss_classifier: 0.2487 (0.2497)  loss_box_reg: 0.1987 (0.2028)  loss_objectness: 0.0723 (0.0688)  loss_rpn_box_reg: 0.0778 (0.0718)  time: 0.2149  data: 0.0061  max mem: 8983\n",
      "Epoch: [2]  [1700/7330]  eta: 0:20:36  lr: 0.016199  loss: 0.5322 (0.5930)  loss_classifier: 0.2230 (0.2497)  loss_box_reg: 0.1833 (0.2028)  loss_objectness: 0.0607 (0.0688)  loss_rpn_box_reg: 0.0722 (0.0717)  time: 0.2199  data: 0.0064  max mem: 8983\n",
      "Epoch: [2]  [1800/7330]  eta: 0:20:13  lr: 0.016149  loss: 0.5798 (0.5929)  loss_classifier: 0.2431 (0.2498)  loss_box_reg: 0.1982 (0.2028)  loss_objectness: 0.0578 (0.0687)  loss_rpn_box_reg: 0.0710 (0.0716)  time: 0.2164  data: 0.0061  max mem: 8983\n",
      "Epoch: [2]  [1900/7330]  eta: 0:19:52  lr: 0.016099  loss: 0.6256 (0.5930)  loss_classifier: 0.2694 (0.2498)  loss_box_reg: 0.2070 (0.2030)  loss_objectness: 0.0644 (0.0686)  loss_rpn_box_reg: 0.0663 (0.0717)  time: 0.2221  data: 0.0060  max mem: 8983\n",
      "Epoch: [2]  [2000/7330]  eta: 0:19:29  lr: 0.016050  loss: 0.5842 (0.5923)  loss_classifier: 0.2355 (0.2496)  loss_box_reg: 0.2012 (0.2028)  loss_objectness: 0.0663 (0.0685)  loss_rpn_box_reg: 0.0617 (0.0715)  time: 0.2145  data: 0.0062  max mem: 8983\n",
      "Epoch: [2]  [2100/7330]  eta: 0:19:07  lr: 0.015999  loss: 0.5935 (0.5920)  loss_classifier: 0.2479 (0.2494)  loss_box_reg: 0.1911 (0.2026)  loss_objectness: 0.0733 (0.0686)  loss_rpn_box_reg: 0.0743 (0.0715)  time: 0.2152  data: 0.0063  max mem: 8983\n",
      "Epoch: [2]  [2200/7330]  eta: 0:18:45  lr: 0.015949  loss: 0.5845 (0.5920)  loss_classifier: 0.2380 (0.2494)  loss_box_reg: 0.2002 (0.2025)  loss_objectness: 0.0623 (0.0686)  loss_rpn_box_reg: 0.0609 (0.0715)  time: 0.2159  data: 0.0064  max mem: 8983\n",
      "Epoch: [2]  [2300/7330]  eta: 0:18:23  lr: 0.015898  loss: 0.5753 (0.5920)  loss_classifier: 0.2352 (0.2494)  loss_box_reg: 0.1990 (0.2024)  loss_objectness: 0.0628 (0.0686)  loss_rpn_box_reg: 0.0654 (0.0716)  time: 0.2227  data: 0.0061  max mem: 8983\n",
      "Epoch: [2]  [2400/7330]  eta: 0:18:01  lr: 0.015848  loss: 0.5563 (0.5916)  loss_classifier: 0.2343 (0.2493)  loss_box_reg: 0.1946 (0.2022)  loss_objectness: 0.0631 (0.0686)  loss_rpn_box_reg: 0.0607 (0.0715)  time: 0.2233  data: 0.0063  max mem: 8983\n",
      "Epoch: [2]  [2500/7330]  eta: 0:17:39  lr: 0.015796  loss: 0.5737 (0.5920)  loss_classifier: 0.2352 (0.2496)  loss_box_reg: 0.1956 (0.2025)  loss_objectness: 0.0727 (0.0685)  loss_rpn_box_reg: 0.0576 (0.0714)  time: 0.2159  data: 0.0063  max mem: 8983\n",
      "Epoch: [2]  [2600/7330]  eta: 0:17:18  lr: 0.015745  loss: 0.6045 (0.5928)  loss_classifier: 0.2484 (0.2499)  loss_box_reg: 0.2035 (0.2028)  loss_objectness: 0.0645 (0.0686)  loss_rpn_box_reg: 0.0654 (0.0714)  time: 0.2216  data: 0.0062  max mem: 8983\n",
      "Epoch: [2]  [2700/7330]  eta: 0:16:56  lr: 0.015694  loss: 0.5747 (0.5925)  loss_classifier: 0.2365 (0.2499)  loss_box_reg: 0.2124 (0.2028)  loss_objectness: 0.0628 (0.0685)  loss_rpn_box_reg: 0.0599 (0.0712)  time: 0.2197  data: 0.0064  max mem: 8983\n",
      "Epoch: [2]  [2800/7330]  eta: 0:16:33  lr: 0.015642  loss: 0.5692 (0.5922)  loss_classifier: 0.2363 (0.2498)  loss_box_reg: 0.1917 (0.2028)  loss_objectness: 0.0592 (0.0685)  loss_rpn_box_reg: 0.0528 (0.0711)  time: 0.2172  data: 0.0061  max mem: 8983\n",
      "Epoch: [2]  [2900/7330]  eta: 0:16:12  lr: 0.015590  loss: 0.5696 (0.5921)  loss_classifier: 0.2459 (0.2497)  loss_box_reg: 0.1973 (0.2027)  loss_objectness: 0.0637 (0.0685)  loss_rpn_box_reg: 0.0630 (0.0712)  time: 0.2159  data: 0.0062  max mem: 8983\n",
      "Epoch: [2]  [3000/7330]  eta: 0:15:50  lr: 0.015538  loss: 0.5363 (0.5920)  loss_classifier: 0.2209 (0.2496)  loss_box_reg: 0.1895 (0.2027)  loss_objectness: 0.0635 (0.0684)  loss_rpn_box_reg: 0.0680 (0.0713)  time: 0.2258  data: 0.0065  max mem: 8983\n",
      "Epoch: [2]  [3100/7330]  eta: 0:15:28  lr: 0.015485  loss: 0.5595 (0.5916)  loss_classifier: 0.2404 (0.2495)  loss_box_reg: 0.1903 (0.2026)  loss_objectness: 0.0598 (0.0683)  loss_rpn_box_reg: 0.0639 (0.0713)  time: 0.2238  data: 0.0061  max mem: 8983\n",
      "Epoch: [2]  [3200/7330]  eta: 0:15:06  lr: 0.015433  loss: 0.5950 (0.5915)  loss_classifier: 0.2479 (0.2494)  loss_box_reg: 0.1988 (0.2025)  loss_objectness: 0.0626 (0.0683)  loss_rpn_box_reg: 0.0670 (0.0713)  time: 0.2164  data: 0.0063  max mem: 8983\n",
      "Epoch: [2]  [3300/7330]  eta: 0:14:44  lr: 0.015380  loss: 0.6065 (0.5916)  loss_classifier: 0.2605 (0.2495)  loss_box_reg: 0.2013 (0.2025)  loss_objectness: 0.0682 (0.0683)  loss_rpn_box_reg: 0.0664 (0.0713)  time: 0.2177  data: 0.0061  max mem: 8983\n",
      "Epoch: [2]  [3400/7330]  eta: 0:14:22  lr: 0.015327  loss: 0.5617 (0.5910)  loss_classifier: 0.2242 (0.2491)  loss_box_reg: 0.1873 (0.2022)  loss_objectness: 0.0637 (0.0683)  loss_rpn_box_reg: 0.0577 (0.0713)  time: 0.2194  data: 0.0061  max mem: 8983\n",
      "Epoch: [2]  [3500/7330]  eta: 0:14:00  lr: 0.015273  loss: 0.6116 (0.5913)  loss_classifier: 0.2570 (0.2493)  loss_box_reg: 0.2052 (0.2024)  loss_objectness: 0.0711 (0.0684)  loss_rpn_box_reg: 0.0615 (0.0712)  time: 0.2198  data: 0.0063  max mem: 8983\n",
      "Epoch: [2]  [3600/7330]  eta: 0:13:38  lr: 0.015220  loss: 0.5445 (0.5914)  loss_classifier: 0.2370 (0.2493)  loss_box_reg: 0.1871 (0.2024)  loss_objectness: 0.0641 (0.0684)  loss_rpn_box_reg: 0.0575 (0.0713)  time: 0.2226  data: 0.0063  max mem: 8983\n",
      "Epoch: [2]  [3700/7330]  eta: 0:13:16  lr: 0.015166  loss: 0.5390 (0.5912)  loss_classifier: 0.1976 (0.2492)  loss_box_reg: 0.1742 (0.2024)  loss_objectness: 0.0587 (0.0684)  loss_rpn_box_reg: 0.0569 (0.0712)  time: 0.2212  data: 0.0066  max mem: 8983\n",
      "Epoch: [2]  [3800/7330]  eta: 0:12:54  lr: 0.015112  loss: 0.5497 (0.5912)  loss_classifier: 0.2300 (0.2492)  loss_box_reg: 0.1950 (0.2024)  loss_objectness: 0.0610 (0.0683)  loss_rpn_box_reg: 0.0608 (0.0713)  time: 0.2235  data: 0.0066  max mem: 8983\n",
      "Epoch: [2]  [3900/7330]  eta: 0:12:32  lr: 0.015058  loss: 0.5773 (0.5909)  loss_classifier: 0.2491 (0.2490)  loss_box_reg: 0.2064 (0.2024)  loss_objectness: 0.0638 (0.0683)  loss_rpn_box_reg: 0.0601 (0.0712)  time: 0.2156  data: 0.0065  max mem: 8983\n",
      "Epoch: [2]  [4000/7330]  eta: 0:12:10  lr: 0.015004  loss: 0.5787 (0.5907)  loss_classifier: 0.2409 (0.2489)  loss_box_reg: 0.2083 (0.2024)  loss_objectness: 0.0642 (0.0682)  loss_rpn_box_reg: 0.0700 (0.0712)  time: 0.2240  data: 0.0066  max mem: 8983\n",
      "Epoch: [2]  [4100/7330]  eta: 0:11:48  lr: 0.014949  loss: 0.5878 (0.5907)  loss_classifier: 0.2396 (0.2488)  loss_box_reg: 0.1999 (0.2024)  loss_objectness: 0.0618 (0.0683)  loss_rpn_box_reg: 0.0692 (0.0712)  time: 0.2197  data: 0.0062  max mem: 8983\n",
      "Epoch: [2]  [4200/7330]  eta: 0:11:26  lr: 0.014895  loss: 0.5888 (0.5907)  loss_classifier: 0.2591 (0.2487)  loss_box_reg: 0.2049 (0.2024)  loss_objectness: 0.0683 (0.0683)  loss_rpn_box_reg: 0.0740 (0.0713)  time: 0.2224  data: 0.0061  max mem: 8983\n",
      "Epoch: [2]  [4300/7330]  eta: 0:11:04  lr: 0.014840  loss: 0.6108 (0.5909)  loss_classifier: 0.2557 (0.2488)  loss_box_reg: 0.2203 (0.2025)  loss_objectness: 0.0670 (0.0683)  loss_rpn_box_reg: 0.0708 (0.0713)  time: 0.2183  data: 0.0065  max mem: 8983\n",
      "Epoch: [2]  [4400/7330]  eta: 0:10:42  lr: 0.014785  loss: 0.5413 (0.5905)  loss_classifier: 0.2350 (0.2487)  loss_box_reg: 0.1849 (0.2023)  loss_objectness: 0.0591 (0.0682)  loss_rpn_box_reg: 0.0654 (0.0713)  time: 0.2171  data: 0.0063  max mem: 8983\n",
      "Epoch: [2]  [4500/7330]  eta: 0:10:20  lr: 0.014729  loss: 0.5901 (0.5904)  loss_classifier: 0.2645 (0.2487)  loss_box_reg: 0.1928 (0.2022)  loss_objectness: 0.0634 (0.0681)  loss_rpn_box_reg: 0.0758 (0.0714)  time: 0.2211  data: 0.0061  max mem: 8983\n",
      "Epoch: [2]  [4600/7330]  eta: 0:09:58  lr: 0.014674  loss: 0.5763 (0.5905)  loss_classifier: 0.2386 (0.2487)  loss_box_reg: 0.1983 (0.2023)  loss_objectness: 0.0615 (0.0681)  loss_rpn_box_reg: 0.0772 (0.0714)  time: 0.2112  data: 0.0061  max mem: 8983\n",
      "Epoch: [2]  [4700/7330]  eta: 0:09:36  lr: 0.014618  loss: 0.5891 (0.5905)  loss_classifier: 0.2449 (0.2487)  loss_box_reg: 0.2047 (0.2024)  loss_objectness: 0.0592 (0.0681)  loss_rpn_box_reg: 0.0616 (0.0713)  time: 0.2182  data: 0.0063  max mem: 8983\n",
      "Epoch: [2]  [4800/7330]  eta: 0:09:14  lr: 0.014563  loss: 0.5791 (0.5904)  loss_classifier: 0.2485 (0.2486)  loss_box_reg: 0.2016 (0.2024)  loss_objectness: 0.0694 (0.0681)  loss_rpn_box_reg: 0.0749 (0.0714)  time: 0.2179  data: 0.0064  max mem: 8983\n",
      "Epoch: [2]  [4900/7330]  eta: 0:08:52  lr: 0.014507  loss: 0.6048 (0.5901)  loss_classifier: 0.2566 (0.2485)  loss_box_reg: 0.2085 (0.2023)  loss_objectness: 0.0676 (0.0680)  loss_rpn_box_reg: 0.0742 (0.0714)  time: 0.2191  data: 0.0067  max mem: 8983\n",
      "Epoch: [2]  [5000/7330]  eta: 0:08:31  lr: 0.014450  loss: 0.5517 (0.5901)  loss_classifier: 0.2159 (0.2485)  loss_box_reg: 0.1796 (0.2023)  loss_objectness: 0.0600 (0.0679)  loss_rpn_box_reg: 0.0663 (0.0713)  time: 0.2228  data: 0.0062  max mem: 8983\n",
      "Epoch: [2]  [5100/7330]  eta: 0:08:09  lr: 0.014394  loss: 0.5512 (0.5898)  loss_classifier: 0.2271 (0.2484)  loss_box_reg: 0.1885 (0.2022)  loss_objectness: 0.0654 (0.0679)  loss_rpn_box_reg: 0.0621 (0.0712)  time: 0.2211  data: 0.0069  max mem: 8983\n",
      "Epoch: [2]  [5200/7330]  eta: 0:07:47  lr: 0.014338  loss: 0.5768 (0.5898)  loss_classifier: 0.2328 (0.2484)  loss_box_reg: 0.2092 (0.2022)  loss_objectness: 0.0590 (0.0679)  loss_rpn_box_reg: 0.0889 (0.0712)  time: 0.2183  data: 0.0067  max mem: 8983\n",
      "Epoch: [2]  [5300/7330]  eta: 0:07:25  lr: 0.014281  loss: 0.6070 (0.5901)  loss_classifier: 0.2417 (0.2485)  loss_box_reg: 0.2076 (0.2024)  loss_objectness: 0.0641 (0.0679)  loss_rpn_box_reg: 0.0684 (0.0712)  time: 0.2118  data: 0.0065  max mem: 8983\n",
      "Epoch: [2]  [5400/7330]  eta: 0:07:03  lr: 0.014224  loss: 0.5630 (0.5897)  loss_classifier: 0.2290 (0.2483)  loss_box_reg: 0.1892 (0.2023)  loss_objectness: 0.0583 (0.0679)  loss_rpn_box_reg: 0.0719 (0.0713)  time: 0.2237  data: 0.0063  max mem: 8983\n",
      "Epoch: [2]  [5500/7330]  eta: 0:06:41  lr: 0.014167  loss: 0.5833 (0.5895)  loss_classifier: 0.2547 (0.2483)  loss_box_reg: 0.2021 (0.2023)  loss_objectness: 0.0582 (0.0678)  loss_rpn_box_reg: 0.0607 (0.0712)  time: 0.2206  data: 0.0064  max mem: 8983\n",
      "Epoch: [2]  [5600/7330]  eta: 0:06:19  lr: 0.014110  loss: 0.5873 (0.5896)  loss_classifier: 0.2351 (0.2483)  loss_box_reg: 0.2137 (0.2023)  loss_objectness: 0.0681 (0.0678)  loss_rpn_box_reg: 0.0721 (0.0711)  time: 0.2184  data: 0.0064  max mem: 8983\n",
      "Epoch: [2]  [5700/7330]  eta: 0:05:57  lr: 0.014052  loss: 0.5632 (0.5895)  loss_classifier: 0.2451 (0.2483)  loss_box_reg: 0.1759 (0.2022)  loss_objectness: 0.0671 (0.0679)  loss_rpn_box_reg: 0.0629 (0.0712)  time: 0.2200  data: 0.0062  max mem: 8983\n",
      "Epoch: [2]  [5800/7330]  eta: 0:05:35  lr: 0.013995  loss: 0.6012 (0.5896)  loss_classifier: 0.2475 (0.2483)  loss_box_reg: 0.2037 (0.2022)  loss_objectness: 0.0665 (0.0679)  loss_rpn_box_reg: 0.0693 (0.0712)  time: 0.2203  data: 0.0062  max mem: 8983\n",
      "Epoch: [2]  [5900/7330]  eta: 0:05:13  lr: 0.013937  loss: 0.5650 (0.5894)  loss_classifier: 0.2398 (0.2482)  loss_box_reg: 0.1970 (0.2022)  loss_objectness: 0.0589 (0.0679)  loss_rpn_box_reg: 0.0655 (0.0712)  time: 0.2136  data: 0.0066  max mem: 8983\n",
      "Epoch: [2]  [6000/7330]  eta: 0:04:51  lr: 0.013879  loss: 0.5953 (0.5893)  loss_classifier: 0.2493 (0.2482)  loss_box_reg: 0.2212 (0.2022)  loss_objectness: 0.0640 (0.0678)  loss_rpn_box_reg: 0.0630 (0.0712)  time: 0.2175  data: 0.0067  max mem: 8983\n",
      "Epoch: [2]  [6100/7330]  eta: 0:04:29  lr: 0.013821  loss: 0.6135 (0.5893)  loss_classifier: 0.2530 (0.2481)  loss_box_reg: 0.2246 (0.2023)  loss_objectness: 0.0602 (0.0678)  loss_rpn_box_reg: 0.0605 (0.0711)  time: 0.2214  data: 0.0063  max mem: 8983\n",
      "Epoch: [2]  [6200/7330]  eta: 0:04:07  lr: 0.013763  loss: 0.5524 (0.5892)  loss_classifier: 0.2309 (0.2481)  loss_box_reg: 0.1891 (0.2022)  loss_objectness: 0.0630 (0.0678)  loss_rpn_box_reg: 0.0695 (0.0711)  time: 0.2165  data: 0.0065  max mem: 8983\n",
      "Epoch: [2]  [6300/7330]  eta: 0:03:46  lr: 0.013705  loss: 0.5573 (0.5892)  loss_classifier: 0.2285 (0.2481)  loss_box_reg: 0.1882 (0.2022)  loss_objectness: 0.0655 (0.0678)  loss_rpn_box_reg: 0.0587 (0.0711)  time: 0.2197  data: 0.0062  max mem: 8983\n",
      "Epoch: [2]  [6400/7330]  eta: 0:03:24  lr: 0.013646  loss: 0.5904 (0.5892)  loss_classifier: 0.2301 (0.2481)  loss_box_reg: 0.1963 (0.2023)  loss_objectness: 0.0681 (0.0678)  loss_rpn_box_reg: 0.0679 (0.0711)  time: 0.2206  data: 0.0063  max mem: 8983\n",
      "Epoch: [2]  [6500/7330]  eta: 0:03:02  lr: 0.013588  loss: 0.5569 (0.5889)  loss_classifier: 0.2380 (0.2479)  loss_box_reg: 0.1994 (0.2022)  loss_objectness: 0.0603 (0.0678)  loss_rpn_box_reg: 0.0640 (0.0711)  time: 0.2213  data: 0.0063  max mem: 8983\n",
      "Epoch: [2]  [6600/7330]  eta: 0:02:40  lr: 0.013529  loss: 0.5603 (0.5886)  loss_classifier: 0.2418 (0.2477)  loss_box_reg: 0.1770 (0.2021)  loss_objectness: 0.0629 (0.0678)  loss_rpn_box_reg: 0.0722 (0.0710)  time: 0.2221  data: 0.0061  max mem: 8983\n",
      "Epoch: [2]  [6700/7330]  eta: 0:02:18  lr: 0.013470  loss: 0.5921 (0.5885)  loss_classifier: 0.2532 (0.2477)  loss_box_reg: 0.1959 (0.2020)  loss_objectness: 0.0671 (0.0678)  loss_rpn_box_reg: 0.0615 (0.0711)  time: 0.2208  data: 0.0062  max mem: 8983\n",
      "Epoch: [2]  [6800/7330]  eta: 0:01:56  lr: 0.013411  loss: 0.5739 (0.5886)  loss_classifier: 0.2330 (0.2477)  loss_box_reg: 0.2053 (0.2021)  loss_objectness: 0.0667 (0.0678)  loss_rpn_box_reg: 0.0614 (0.0710)  time: 0.2136  data: 0.0061  max mem: 8983\n",
      "Epoch: [2]  [6900/7330]  eta: 0:01:34  lr: 0.013352  loss: 0.5578 (0.5883)  loss_classifier: 0.2365 (0.2476)  loss_box_reg: 0.1874 (0.2020)  loss_objectness: 0.0639 (0.0678)  loss_rpn_box_reg: 0.0653 (0.0710)  time: 0.2221  data: 0.0065  max mem: 8983\n",
      "Epoch: [2]  [7000/7330]  eta: 0:01:12  lr: 0.013293  loss: 0.5908 (0.5880)  loss_classifier: 0.2422 (0.2474)  loss_box_reg: 0.2010 (0.2019)  loss_objectness: 0.0739 (0.0677)  loss_rpn_box_reg: 0.0768 (0.0710)  time: 0.2152  data: 0.0063  max mem: 8983\n",
      "Epoch: [2]  [7100/7330]  eta: 0:00:50  lr: 0.013234  loss: 0.5628 (0.5879)  loss_classifier: 0.2441 (0.2474)  loss_box_reg: 0.2047 (0.2019)  loss_objectness: 0.0666 (0.0677)  loss_rpn_box_reg: 0.0559 (0.0709)  time: 0.2166  data: 0.0062  max mem: 8983\n",
      "Epoch: [2]  [7200/7330]  eta: 0:00:28  lr: 0.013174  loss: 0.5225 (0.5878)  loss_classifier: 0.2244 (0.2474)  loss_box_reg: 0.1773 (0.2019)  loss_objectness: 0.0581 (0.0677)  loss_rpn_box_reg: 0.0730 (0.0709)  time: 0.2203  data: 0.0065  max mem: 8983\n",
      "Epoch: [2]  [7300/7330]  eta: 0:00:06  lr: 0.013114  loss: 0.5267 (0.5875)  loss_classifier: 0.2198 (0.2473)  loss_box_reg: 0.1860 (0.2018)  loss_objectness: 0.0574 (0.0676)  loss_rpn_box_reg: 0.0634 (0.0709)  time: 0.2206  data: 0.0064  max mem: 8983\n",
      "Epoch: [2]  [7329/7330]  eta: 0:00:00  lr: 0.013097  loss: 0.5463 (0.5874)  loss_classifier: 0.2246 (0.2472)  loss_box_reg: 0.1930 (0.2018)  loss_objectness: 0.0578 (0.0676)  loss_rpn_box_reg: 0.0580 (0.0708)  time: 0.2095  data: 0.0063  max mem: 8983\n",
      "Epoch: [2] Total time: 0:26:48 (0.2194 s / it)\n",
      "Test:  [  0/313]  eta: 0:02:45  model_time: 0.1371 (0.1371)  evaluator_time: 0.0264 (0.0264)  time: 0.5274  data: 0.3549  max mem: 8983\n",
      "Test:  [312/313]  eta: 0:00:00  model_time: 0.1283 (0.1299)  evaluator_time: 0.0192 (0.0265)  time: 0.1586  data: 0.0063  max mem: 8983\n",
      "Test: Total time: 0:00:53 (0.1705 s / it)\n",
      "Averaged stats: model_time: 0.1283 (0.1299)  evaluator_time: 0.0192 (0.0265)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.389\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.726\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.396\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.303\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.365\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.540\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.317\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.397\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.405\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.318\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.364\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.560\n",
      "Epoch: [3]  [   0/7330]  eta: 1:46:13  lr: 0.013096  loss: 0.7804 (0.7804)  loss_classifier: 0.3244 (0.3244)  loss_box_reg: 0.2898 (0.2898)  loss_objectness: 0.0759 (0.0759)  loss_rpn_box_reg: 0.0904 (0.0904)  time: 0.8695  data: 0.6476  max mem: 8983\n",
      "Epoch: [3]  [ 100/7330]  eta: 0:27:17  lr: 0.013037  loss: 0.5649 (0.5734)  loss_classifier: 0.2447 (0.2395)  loss_box_reg: 0.1907 (0.1979)  loss_objectness: 0.0547 (0.0649)  loss_rpn_box_reg: 0.0712 (0.0711)  time: 0.2203  data: 0.0066  max mem: 8983\n",
      "Epoch: [3]  [ 200/7330]  eta: 0:26:28  lr: 0.012977  loss: 0.5717 (0.5768)  loss_classifier: 0.2228 (0.2412)  loss_box_reg: 0.1867 (0.2015)  loss_objectness: 0.0615 (0.0641)  loss_rpn_box_reg: 0.0702 (0.0699)  time: 0.2202  data: 0.0070  max mem: 8983\n",
      "Epoch: [3]  [ 300/7330]  eta: 0:25:52  lr: 0.012917  loss: 0.5662 (0.5740)  loss_classifier: 0.2313 (0.2398)  loss_box_reg: 0.1928 (0.2000)  loss_objectness: 0.0668 (0.0648)  loss_rpn_box_reg: 0.0680 (0.0693)  time: 0.2170  data: 0.0065  max mem: 8983\n",
      "Epoch: [3]  [ 400/7330]  eta: 0:25:25  lr: 0.012857  loss: 0.5825 (0.5693)  loss_classifier: 0.2406 (0.2374)  loss_box_reg: 0.2018 (0.1977)  loss_objectness: 0.0608 (0.0648)  loss_rpn_box_reg: 0.0667 (0.0694)  time: 0.2118  data: 0.0063  max mem: 8983\n",
      "Epoch: [3]  [ 500/7330]  eta: 0:25:03  lr: 0.012796  loss: 0.5206 (0.5673)  loss_classifier: 0.2074 (0.2369)  loss_box_reg: 0.1702 (0.1968)  loss_objectness: 0.0569 (0.0646)  loss_rpn_box_reg: 0.0602 (0.0690)  time: 0.2200  data: 0.0065  max mem: 8983\n",
      "Epoch: [3]  [ 600/7330]  eta: 0:24:40  lr: 0.012736  loss: 0.5835 (0.5679)  loss_classifier: 0.2513 (0.2378)  loss_box_reg: 0.2032 (0.1973)  loss_objectness: 0.0688 (0.0647)  loss_rpn_box_reg: 0.0549 (0.0682)  time: 0.2161  data: 0.0068  max mem: 8983\n",
      "Epoch: [3]  [ 700/7330]  eta: 0:24:19  lr: 0.012675  loss: 0.5497 (0.5665)  loss_classifier: 0.2328 (0.2371)  loss_box_reg: 0.1984 (0.1968)  loss_objectness: 0.0554 (0.0642)  loss_rpn_box_reg: 0.0618 (0.0684)  time: 0.2206  data: 0.0067  max mem: 8983\n",
      "Epoch: [3]  [ 800/7330]  eta: 0:23:56  lr: 0.012615  loss: 0.5405 (0.5674)  loss_classifier: 0.2293 (0.2377)  loss_box_reg: 0.1926 (0.1971)  loss_objectness: 0.0672 (0.0644)  loss_rpn_box_reg: 0.0746 (0.0682)  time: 0.2161  data: 0.0069  max mem: 8983\n",
      "Epoch: [3]  [ 900/7330]  eta: 0:23:33  lr: 0.012554  loss: 0.5952 (0.5671)  loss_classifier: 0.2374 (0.2375)  loss_box_reg: 0.2047 (0.1967)  loss_objectness: 0.0700 (0.0645)  loss_rpn_box_reg: 0.0686 (0.0684)  time: 0.2194  data: 0.0068  max mem: 8983\n",
      "Epoch: [3]  [1000/7330]  eta: 0:23:11  lr: 0.012493  loss: 0.5394 (0.5682)  loss_classifier: 0.2294 (0.2378)  loss_box_reg: 0.1942 (0.1969)  loss_objectness: 0.0589 (0.0648)  loss_rpn_box_reg: 0.0575 (0.0688)  time: 0.2209  data: 0.0064  max mem: 8983\n",
      "Epoch: [3]  [1100/7330]  eta: 0:22:49  lr: 0.012432  loss: 0.5537 (0.5671)  loss_classifier: 0.2421 (0.2374)  loss_box_reg: 0.1947 (0.1965)  loss_objectness: 0.0561 (0.0645)  loss_rpn_box_reg: 0.0688 (0.0686)  time: 0.2211  data: 0.0063  max mem: 8983\n",
      "Epoch: [3]  [1200/7330]  eta: 0:22:28  lr: 0.012371  loss: 0.5224 (0.5660)  loss_classifier: 0.2169 (0.2370)  loss_box_reg: 0.1830 (0.1962)  loss_objectness: 0.0552 (0.0643)  loss_rpn_box_reg: 0.0575 (0.0686)  time: 0.2234  data: 0.0065  max mem: 8983\n",
      "Epoch: [3]  [1300/7330]  eta: 0:22:06  lr: 0.012310  loss: 0.5562 (0.5658)  loss_classifier: 0.2243 (0.2368)  loss_box_reg: 0.1943 (0.1963)  loss_objectness: 0.0613 (0.0641)  loss_rpn_box_reg: 0.0600 (0.0686)  time: 0.2190  data: 0.0066  max mem: 8983\n",
      "Epoch: [3]  [1400/7330]  eta: 0:21:45  lr: 0.012249  loss: 0.6011 (0.5669)  loss_classifier: 0.2408 (0.2373)  loss_box_reg: 0.2182 (0.1968)  loss_objectness: 0.0690 (0.0640)  loss_rpn_box_reg: 0.0690 (0.0687)  time: 0.2206  data: 0.0067  max mem: 8983\n",
      "Epoch: [3]  [1500/7330]  eta: 0:21:23  lr: 0.012188  loss: 0.5179 (0.5666)  loss_classifier: 0.2104 (0.2370)  loss_box_reg: 0.1819 (0.1967)  loss_objectness: 0.0546 (0.0640)  loss_rpn_box_reg: 0.0648 (0.0689)  time: 0.2219  data: 0.0064  max mem: 8983\n",
      "Epoch: [3]  [1600/7330]  eta: 0:21:01  lr: 0.012127  loss: 0.5609 (0.5667)  loss_classifier: 0.2363 (0.2371)  loss_box_reg: 0.1932 (0.1967)  loss_objectness: 0.0620 (0.0641)  loss_rpn_box_reg: 0.0646 (0.0688)  time: 0.2206  data: 0.0065  max mem: 8983\n",
      "Epoch: [3]  [1700/7330]  eta: 0:20:39  lr: 0.012065  loss: 0.5518 (0.5666)  loss_classifier: 0.2256 (0.2368)  loss_box_reg: 0.1943 (0.1968)  loss_objectness: 0.0591 (0.0642)  loss_rpn_box_reg: 0.0679 (0.0688)  time: 0.2181  data: 0.0063  max mem: 8983\n",
      "Epoch: [3]  [1800/7330]  eta: 0:20:17  lr: 0.012004  loss: 0.5508 (0.5662)  loss_classifier: 0.2276 (0.2367)  loss_box_reg: 0.1930 (0.1968)  loss_objectness: 0.0581 (0.0641)  loss_rpn_box_reg: 0.0636 (0.0687)  time: 0.2183  data: 0.0062  max mem: 8983\n",
      "Epoch: [3]  [1900/7330]  eta: 0:19:55  lr: 0.011942  loss: 0.5548 (0.5657)  loss_classifier: 0.2437 (0.2365)  loss_box_reg: 0.1894 (0.1965)  loss_objectness: 0.0671 (0.0639)  loss_rpn_box_reg: 0.0748 (0.0687)  time: 0.2223  data: 0.0061  max mem: 8983\n",
      "Epoch: [3]  [2000/7330]  eta: 0:19:33  lr: 0.011880  loss: 0.5830 (0.5655)  loss_classifier: 0.2426 (0.2364)  loss_box_reg: 0.2019 (0.1964)  loss_objectness: 0.0638 (0.0639)  loss_rpn_box_reg: 0.0714 (0.0688)  time: 0.2202  data: 0.0064  max mem: 8983\n",
      "Epoch: [3]  [2100/7330]  eta: 0:19:10  lr: 0.011819  loss: 0.5849 (0.5654)  loss_classifier: 0.2270 (0.2362)  loss_box_reg: 0.2071 (0.1966)  loss_objectness: 0.0624 (0.0638)  loss_rpn_box_reg: 0.0594 (0.0688)  time: 0.2187  data: 0.0064  max mem: 8983\n",
      "Epoch: [3]  [2200/7330]  eta: 0:18:48  lr: 0.011757  loss: 0.5725 (0.5654)  loss_classifier: 0.2361 (0.2362)  loss_box_reg: 0.2016 (0.1968)  loss_objectness: 0.0625 (0.0638)  loss_rpn_box_reg: 0.0658 (0.0686)  time: 0.2217  data: 0.0063  max mem: 8983\n",
      "Epoch: [3]  [2300/7330]  eta: 0:18:26  lr: 0.011695  loss: 0.5474 (0.5656)  loss_classifier: 0.2266 (0.2363)  loss_box_reg: 0.2062 (0.1970)  loss_objectness: 0.0659 (0.0638)  loss_rpn_box_reg: 0.0592 (0.0685)  time: 0.2227  data: 0.0062  max mem: 8983\n",
      "Epoch: [3]  [2400/7330]  eta: 0:18:03  lr: 0.011633  loss: 0.5193 (0.5656)  loss_classifier: 0.2227 (0.2363)  loss_box_reg: 0.1902 (0.1971)  loss_objectness: 0.0667 (0.0638)  loss_rpn_box_reg: 0.0612 (0.0684)  time: 0.2147  data: 0.0061  max mem: 8983\n",
      "Epoch: [3]  [2500/7330]  eta: 0:17:41  lr: 0.011571  loss: 0.5607 (0.5654)  loss_classifier: 0.2431 (0.2363)  loss_box_reg: 0.2034 (0.1970)  loss_objectness: 0.0655 (0.0637)  loss_rpn_box_reg: 0.0629 (0.0683)  time: 0.2163  data: 0.0063  max mem: 8983\n",
      "Epoch: [3]  [2600/7330]  eta: 0:17:19  lr: 0.011509  loss: 0.5667 (0.5656)  loss_classifier: 0.2360 (0.2364)  loss_box_reg: 0.1958 (0.1970)  loss_objectness: 0.0614 (0.0638)  loss_rpn_box_reg: 0.0610 (0.0684)  time: 0.2182  data: 0.0064  max mem: 8983\n",
      "Epoch: [3]  [2700/7330]  eta: 0:16:57  lr: 0.011447  loss: 0.5237 (0.5652)  loss_classifier: 0.2279 (0.2363)  loss_box_reg: 0.1895 (0.1970)  loss_objectness: 0.0570 (0.0638)  loss_rpn_box_reg: 0.0660 (0.0683)  time: 0.2209  data: 0.0066  max mem: 8983\n",
      "Epoch: [3]  [2800/7330]  eta: 0:16:35  lr: 0.011385  loss: 0.5414 (0.5655)  loss_classifier: 0.2398 (0.2365)  loss_box_reg: 0.1931 (0.1972)  loss_objectness: 0.0565 (0.0637)  loss_rpn_box_reg: 0.0560 (0.0681)  time: 0.2222  data: 0.0063  max mem: 8983\n",
      "Epoch: [3]  [2900/7330]  eta: 0:16:13  lr: 0.011322  loss: 0.5418 (0.5656)  loss_classifier: 0.2291 (0.2366)  loss_box_reg: 0.1898 (0.1973)  loss_objectness: 0.0577 (0.0637)  loss_rpn_box_reg: 0.0707 (0.0681)  time: 0.2216  data: 0.0065  max mem: 8983\n",
      "Epoch: [3]  [3000/7330]  eta: 0:15:51  lr: 0.011260  loss: 0.5740 (0.5653)  loss_classifier: 0.2373 (0.2364)  loss_box_reg: 0.1955 (0.1971)  loss_objectness: 0.0618 (0.0637)  loss_rpn_box_reg: 0.0701 (0.0681)  time: 0.2210  data: 0.0063  max mem: 8983\n",
      "Epoch: [3]  [3100/7330]  eta: 0:15:29  lr: 0.011198  loss: 0.5473 (0.5650)  loss_classifier: 0.2203 (0.2362)  loss_box_reg: 0.1904 (0.1970)  loss_objectness: 0.0554 (0.0636)  loss_rpn_box_reg: 0.0727 (0.0681)  time: 0.2231  data: 0.0064  max mem: 8983\n",
      "Epoch: [3]  [3200/7330]  eta: 0:15:08  lr: 0.011135  loss: 0.5498 (0.5644)  loss_classifier: 0.2293 (0.2359)  loss_box_reg: 0.1912 (0.1968)  loss_objectness: 0.0551 (0.0635)  loss_rpn_box_reg: 0.0648 (0.0682)  time: 0.2183  data: 0.0066  max mem: 8983\n",
      "Epoch: [3]  [3300/7330]  eta: 0:14:46  lr: 0.011073  loss: 0.5878 (0.5642)  loss_classifier: 0.2461 (0.2358)  loss_box_reg: 0.2051 (0.1968)  loss_objectness: 0.0580 (0.0635)  loss_rpn_box_reg: 0.0617 (0.0681)  time: 0.2198  data: 0.0063  max mem: 8983\n",
      "Epoch: [3]  [3400/7330]  eta: 0:14:24  lr: 0.011010  loss: 0.5854 (0.5645)  loss_classifier: 0.2357 (0.2360)  loss_box_reg: 0.2079 (0.1969)  loss_objectness: 0.0619 (0.0635)  loss_rpn_box_reg: 0.0719 (0.0682)  time: 0.2132  data: 0.0063  max mem: 8983\n",
      "Epoch: [3]  [3500/7330]  eta: 0:14:01  lr: 0.010948  loss: 0.5545 (0.5641)  loss_classifier: 0.2351 (0.2357)  loss_box_reg: 0.1990 (0.1969)  loss_objectness: 0.0575 (0.0634)  loss_rpn_box_reg: 0.0563 (0.0682)  time: 0.2214  data: 0.0065  max mem: 8983\n",
      "Epoch: [3]  [3600/7330]  eta: 0:13:39  lr: 0.010885  loss: 0.5426 (0.5639)  loss_classifier: 0.2449 (0.2357)  loss_box_reg: 0.1934 (0.1968)  loss_objectness: 0.0567 (0.0633)  loss_rpn_box_reg: 0.0644 (0.0681)  time: 0.2194  data: 0.0062  max mem: 8983\n",
      "Epoch: [3]  [3700/7330]  eta: 0:13:17  lr: 0.010823  loss: 0.5587 (0.5641)  loss_classifier: 0.2345 (0.2358)  loss_box_reg: 0.2058 (0.1968)  loss_objectness: 0.0587 (0.0633)  loss_rpn_box_reg: 0.0552 (0.0682)  time: 0.2249  data: 0.0062  max mem: 8983\n",
      "Epoch: [3]  [3800/7330]  eta: 0:12:55  lr: 0.010760  loss: 0.5497 (0.5638)  loss_classifier: 0.2109 (0.2356)  loss_box_reg: 0.1795 (0.1968)  loss_objectness: 0.0529 (0.0632)  loss_rpn_box_reg: 0.0580 (0.0683)  time: 0.2220  data: 0.0066  max mem: 8983\n",
      "Epoch: [3]  [3900/7330]  eta: 0:12:33  lr: 0.010697  loss: 0.5820 (0.5641)  loss_classifier: 0.2473 (0.2357)  loss_box_reg: 0.2118 (0.1970)  loss_objectness: 0.0596 (0.0631)  loss_rpn_box_reg: 0.0680 (0.0683)  time: 0.2226  data: 0.0063  max mem: 8983\n",
      "Epoch: [3]  [4000/7330]  eta: 0:12:11  lr: 0.010635  loss: 0.5480 (0.5638)  loss_classifier: 0.2252 (0.2355)  loss_box_reg: 0.2034 (0.1969)  loss_objectness: 0.0596 (0.0631)  loss_rpn_box_reg: 0.0663 (0.0682)  time: 0.2138  data: 0.0062  max mem: 8983\n",
      "Epoch: [3]  [4100/7330]  eta: 0:11:49  lr: 0.010572  loss: 0.5258 (0.5639)  loss_classifier: 0.2265 (0.2356)  loss_box_reg: 0.1938 (0.1969)  loss_objectness: 0.0556 (0.0631)  loss_rpn_box_reg: 0.0639 (0.0682)  time: 0.2183  data: 0.0062  max mem: 8983\n",
      "Epoch: [3]  [4200/7330]  eta: 0:11:27  lr: 0.010509  loss: 0.5624 (0.5640)  loss_classifier: 0.2273 (0.2357)  loss_box_reg: 0.1965 (0.1970)  loss_objectness: 0.0578 (0.0631)  loss_rpn_box_reg: 0.0637 (0.0682)  time: 0.2188  data: 0.0062  max mem: 8983\n",
      "Epoch: [3]  [4300/7330]  eta: 0:11:05  lr: 0.010446  loss: 0.5541 (0.5639)  loss_classifier: 0.2265 (0.2356)  loss_box_reg: 0.1873 (0.1970)  loss_objectness: 0.0602 (0.0631)  loss_rpn_box_reg: 0.0646 (0.0681)  time: 0.2155  data: 0.0064  max mem: 8983\n",
      "Epoch: [3]  [4400/7330]  eta: 0:10:43  lr: 0.010384  loss: 0.5545 (0.5637)  loss_classifier: 0.2376 (0.2355)  loss_box_reg: 0.1997 (0.1970)  loss_objectness: 0.0586 (0.0631)  loss_rpn_box_reg: 0.0739 (0.0681)  time: 0.2190  data: 0.0062  max mem: 8983\n",
      "Epoch: [3]  [4500/7330]  eta: 0:10:21  lr: 0.010321  loss: 0.4783 (0.5631)  loss_classifier: 0.2129 (0.2353)  loss_box_reg: 0.1742 (0.1968)  loss_objectness: 0.0507 (0.0631)  loss_rpn_box_reg: 0.0565 (0.0680)  time: 0.2186  data: 0.0062  max mem: 8983\n",
      "Epoch: [3]  [4600/7330]  eta: 0:09:59  lr: 0.010258  loss: 0.5540 (0.5631)  loss_classifier: 0.2201 (0.2353)  loss_box_reg: 0.1769 (0.1968)  loss_objectness: 0.0593 (0.0631)  loss_rpn_box_reg: 0.0753 (0.0680)  time: 0.2121  data: 0.0062  max mem: 8983\n",
      "Epoch: [3]  [4700/7330]  eta: 0:09:37  lr: 0.010195  loss: 0.5644 (0.5632)  loss_classifier: 0.2275 (0.2353)  loss_box_reg: 0.1908 (0.1968)  loss_objectness: 0.0609 (0.0631)  loss_rpn_box_reg: 0.0584 (0.0680)  time: 0.2198  data: 0.0065  max mem: 8983\n",
      "Epoch: [3]  [4800/7330]  eta: 0:09:15  lr: 0.010132  loss: 0.5728 (0.5630)  loss_classifier: 0.2302 (0.2352)  loss_box_reg: 0.1952 (0.1967)  loss_objectness: 0.0626 (0.0631)  loss_rpn_box_reg: 0.0693 (0.0679)  time: 0.2181  data: 0.0062  max mem: 8983\n",
      "Epoch: [3]  [4900/7330]  eta: 0:08:53  lr: 0.010070  loss: 0.4884 (0.5632)  loss_classifier: 0.2040 (0.2353)  loss_box_reg: 0.1771 (0.1968)  loss_objectness: 0.0583 (0.0632)  loss_rpn_box_reg: 0.0604 (0.0679)  time: 0.2199  data: 0.0060  max mem: 8983\n",
      "Epoch: [3]  [5000/7330]  eta: 0:08:31  lr: 0.010007  loss: 0.5850 (0.5630)  loss_classifier: 0.2441 (0.2353)  loss_box_reg: 0.2071 (0.1967)  loss_objectness: 0.0645 (0.0632)  loss_rpn_box_reg: 0.0539 (0.0679)  time: 0.2223  data: 0.0062  max mem: 8983\n",
      "Epoch: [3]  [5100/7330]  eta: 0:08:09  lr: 0.009944  loss: 0.5719 (0.5630)  loss_classifier: 0.2395 (0.2352)  loss_box_reg: 0.2067 (0.1967)  loss_objectness: 0.0587 (0.0632)  loss_rpn_box_reg: 0.0709 (0.0679)  time: 0.2229  data: 0.0063  max mem: 8983\n",
      "Epoch: [3]  [5200/7330]  eta: 0:07:47  lr: 0.009881  loss: 0.5111 (0.5629)  loss_classifier: 0.2104 (0.2352)  loss_box_reg: 0.1820 (0.1967)  loss_objectness: 0.0621 (0.0631)  loss_rpn_box_reg: 0.0609 (0.0679)  time: 0.2199  data: 0.0063  max mem: 8983\n",
      "Epoch: [3]  [5300/7330]  eta: 0:07:25  lr: 0.009818  loss: 0.5370 (0.5630)  loss_classifier: 0.2335 (0.2352)  loss_box_reg: 0.1915 (0.1967)  loss_objectness: 0.0563 (0.0631)  loss_rpn_box_reg: 0.0648 (0.0680)  time: 0.2180  data: 0.0063  max mem: 8983\n",
      "Epoch: [3]  [5400/7330]  eta: 0:07:03  lr: 0.009755  loss: 0.5572 (0.5627)  loss_classifier: 0.2272 (0.2351)  loss_box_reg: 0.1896 (0.1966)  loss_objectness: 0.0610 (0.0631)  loss_rpn_box_reg: 0.0605 (0.0680)  time: 0.2181  data: 0.0062  max mem: 8983\n",
      "Epoch: [3]  [5500/7330]  eta: 0:06:41  lr: 0.009693  loss: 0.4997 (0.5623)  loss_classifier: 0.2149 (0.2349)  loss_box_reg: 0.1793 (0.1965)  loss_objectness: 0.0561 (0.0631)  loss_rpn_box_reg: 0.0483 (0.0678)  time: 0.2191  data: 0.0061  max mem: 8983\n",
      "Epoch: [3]  [5600/7330]  eta: 0:06:19  lr: 0.009630  loss: 0.5715 (0.5624)  loss_classifier: 0.2437 (0.2350)  loss_box_reg: 0.2024 (0.1966)  loss_objectness: 0.0615 (0.0630)  loss_rpn_box_reg: 0.0675 (0.0678)  time: 0.2211  data: 0.0066  max mem: 8983\n",
      "Epoch: [3]  [5700/7330]  eta: 0:05:57  lr: 0.009567  loss: 0.5567 (0.5623)  loss_classifier: 0.2297 (0.2349)  loss_box_reg: 0.1945 (0.1965)  loss_objectness: 0.0617 (0.0630)  loss_rpn_box_reg: 0.0681 (0.0679)  time: 0.2131  data: 0.0063  max mem: 8983\n",
      "Epoch: [3]  [5800/7330]  eta: 0:05:35  lr: 0.009504  loss: 0.5373 (0.5620)  loss_classifier: 0.2330 (0.2348)  loss_box_reg: 0.1878 (0.1964)  loss_objectness: 0.0572 (0.0630)  loss_rpn_box_reg: 0.0610 (0.0678)  time: 0.2239  data: 0.0062  max mem: 8983\n",
      "Epoch: [3]  [5900/7330]  eta: 0:05:13  lr: 0.009442  loss: 0.5919 (0.5620)  loss_classifier: 0.2408 (0.2348)  loss_box_reg: 0.2002 (0.1964)  loss_objectness: 0.0552 (0.0630)  loss_rpn_box_reg: 0.0678 (0.0678)  time: 0.2236  data: 0.0063  max mem: 8983\n",
      "Epoch: [3]  [6000/7330]  eta: 0:04:51  lr: 0.009379  loss: 0.5361 (0.5616)  loss_classifier: 0.2260 (0.2346)  loss_box_reg: 0.1956 (0.1963)  loss_objectness: 0.0513 (0.0629)  loss_rpn_box_reg: 0.0537 (0.0677)  time: 0.2211  data: 0.0064  max mem: 8983\n",
      "Epoch: [3]  [6100/7330]  eta: 0:04:29  lr: 0.009316  loss: 0.5697 (0.5616)  loss_classifier: 0.2351 (0.2346)  loss_box_reg: 0.2063 (0.1964)  loss_objectness: 0.0573 (0.0629)  loss_rpn_box_reg: 0.0712 (0.0678)  time: 0.2199  data: 0.0065  max mem: 8983\n",
      "Epoch: [3]  [6200/7330]  eta: 0:04:08  lr: 0.009254  loss: 0.6310 (0.5615)  loss_classifier: 0.2549 (0.2346)  loss_box_reg: 0.1955 (0.1963)  loss_objectness: 0.0643 (0.0628)  loss_rpn_box_reg: 0.0830 (0.0678)  time: 0.2163  data: 0.0061  max mem: 8983\n",
      "Epoch: [3]  [6300/7330]  eta: 0:03:46  lr: 0.009191  loss: 0.5083 (0.5614)  loss_classifier: 0.2191 (0.2345)  loss_box_reg: 0.1789 (0.1963)  loss_objectness: 0.0559 (0.0628)  loss_rpn_box_reg: 0.0608 (0.0678)  time: 0.2197  data: 0.0061  max mem: 8983\n",
      "Epoch: [3]  [6400/7330]  eta: 0:03:24  lr: 0.009128  loss: 0.5456 (0.5614)  loss_classifier: 0.2358 (0.2345)  loss_box_reg: 0.1957 (0.1963)  loss_objectness: 0.0618 (0.0628)  loss_rpn_box_reg: 0.0601 (0.0678)  time: 0.2161  data: 0.0064  max mem: 8983\n",
      "Epoch: [3]  [6500/7330]  eta: 0:03:02  lr: 0.009066  loss: 0.5193 (0.5612)  loss_classifier: 0.2099 (0.2344)  loss_box_reg: 0.1979 (0.1962)  loss_objectness: 0.0552 (0.0628)  loss_rpn_box_reg: 0.0669 (0.0678)  time: 0.2212  data: 0.0062  max mem: 8983\n",
      "Epoch: [3]  [6600/7330]  eta: 0:02:40  lr: 0.009003  loss: 0.4938 (0.5609)  loss_classifier: 0.2170 (0.2343)  loss_box_reg: 0.1766 (0.1961)  loss_objectness: 0.0485 (0.0627)  loss_rpn_box_reg: 0.0590 (0.0677)  time: 0.2229  data: 0.0064  max mem: 8983\n",
      "Epoch: [3]  [6700/7330]  eta: 0:02:18  lr: 0.008941  loss: 0.5245 (0.5608)  loss_classifier: 0.2219 (0.2343)  loss_box_reg: 0.1867 (0.1962)  loss_objectness: 0.0590 (0.0627)  loss_rpn_box_reg: 0.0569 (0.0677)  time: 0.2215  data: 0.0061  max mem: 8983\n",
      "Epoch: [3]  [6800/7330]  eta: 0:01:56  lr: 0.008878  loss: 0.5470 (0.5605)  loss_classifier: 0.2255 (0.2342)  loss_box_reg: 0.1818 (0.1961)  loss_objectness: 0.0611 (0.0627)  loss_rpn_box_reg: 0.0726 (0.0677)  time: 0.2202  data: 0.0067  max mem: 8983\n",
      "Epoch: [3]  [6900/7330]  eta: 0:01:34  lr: 0.008816  loss: 0.5831 (0.5604)  loss_classifier: 0.2219 (0.2341)  loss_box_reg: 0.1971 (0.1961)  loss_objectness: 0.0622 (0.0626)  loss_rpn_box_reg: 0.0732 (0.0676)  time: 0.2253  data: 0.0065  max mem: 8983\n",
      "Epoch: [3]  [7000/7330]  eta: 0:01:12  lr: 0.008753  loss: 0.5623 (0.5603)  loss_classifier: 0.2303 (0.2341)  loss_box_reg: 0.1989 (0.1960)  loss_objectness: 0.0618 (0.0626)  loss_rpn_box_reg: 0.0485 (0.0676)  time: 0.2223  data: 0.0065  max mem: 8983\n",
      "Epoch: [3]  [7100/7330]  eta: 0:00:50  lr: 0.008691  loss: 0.5457 (0.5604)  loss_classifier: 0.2251 (0.2341)  loss_box_reg: 0.2004 (0.1961)  loss_objectness: 0.0595 (0.0626)  loss_rpn_box_reg: 0.0564 (0.0676)  time: 0.2247  data: 0.0063  max mem: 8983\n",
      "Epoch: [3]  [7200/7330]  eta: 0:00:28  lr: 0.008629  loss: 0.5554 (0.5604)  loss_classifier: 0.2372 (0.2341)  loss_box_reg: 0.1975 (0.1961)  loss_objectness: 0.0614 (0.0626)  loss_rpn_box_reg: 0.0743 (0.0676)  time: 0.2154  data: 0.0064  max mem: 8983\n",
      "Epoch: [3]  [7300/7330]  eta: 0:00:06  lr: 0.008567  loss: 0.5429 (0.5603)  loss_classifier: 0.2310 (0.2341)  loss_box_reg: 0.1964 (0.1961)  loss_objectness: 0.0607 (0.0626)  loss_rpn_box_reg: 0.0617 (0.0676)  time: 0.2219  data: 0.0061  max mem: 8983\n",
      "Epoch: [3]  [7329/7330]  eta: 0:00:00  lr: 0.008549  loss: 0.5709 (0.5602)  loss_classifier: 0.2455 (0.2340)  loss_box_reg: 0.1981 (0.1960)  loss_objectness: 0.0607 (0.0626)  loss_rpn_box_reg: 0.0668 (0.0676)  time: 0.2075  data: 0.0064  max mem: 8983\n",
      "Epoch: [3] Total time: 0:26:48 (0.2195 s / it)\n",
      "Test:  [  0/313]  eta: 0:02:39  model_time: 0.1373 (0.1373)  evaluator_time: 0.0285 (0.0285)  time: 0.5090  data: 0.3352  max mem: 8983\n",
      "Test:  [312/313]  eta: 0:00:00  model_time: 0.1293 (0.1306)  evaluator_time: 0.0176 (0.0256)  time: 0.1586  data: 0.0062  max mem: 8983\n",
      "Test: Total time: 0:00:53 (0.1704 s / it)\n",
      "Averaged stats: model_time: 0.1293 (0.1306)  evaluator_time: 0.0176 (0.0256)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.416\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.722\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.368\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.192\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.434\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.625\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.324\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.416\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.436\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.218\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.445\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.643\n",
      "Epoch: [4]  [   0/7330]  eta: 1:46:27  lr: 0.008548  loss: 0.4705 (0.4705)  loss_classifier: 0.1979 (0.1979)  loss_box_reg: 0.1765 (0.1765)  loss_objectness: 0.0465 (0.0465)  loss_rpn_box_reg: 0.0496 (0.0496)  time: 0.8714  data: 0.6270  max mem: 8983\n",
      "Epoch: [4]  [ 100/7330]  eta: 0:27:36  lr: 0.008486  loss: 0.5275 (0.5485)  loss_classifier: 0.2109 (0.2266)  loss_box_reg: 0.1930 (0.1974)  loss_objectness: 0.0572 (0.0585)  loss_rpn_box_reg: 0.0616 (0.0659)  time: 0.2190  data: 0.0070  max mem: 8983\n",
      "Epoch: [4]  [ 200/7330]  eta: 0:26:35  lr: 0.008424  loss: 0.5225 (0.5380)  loss_classifier: 0.2140 (0.2227)  loss_box_reg: 0.1921 (0.1910)  loss_objectness: 0.0585 (0.0584)  loss_rpn_box_reg: 0.0612 (0.0659)  time: 0.2210  data: 0.0066  max mem: 8983\n",
      "Epoch: [4]  [ 300/7330]  eta: 0:26:08  lr: 0.008362  loss: 0.5279 (0.5422)  loss_classifier: 0.2047 (0.2238)  loss_box_reg: 0.1848 (0.1931)  loss_objectness: 0.0563 (0.0585)  loss_rpn_box_reg: 0.0668 (0.0668)  time: 0.2234  data: 0.0065  max mem: 8983\n",
      "Epoch: [4]  [ 400/7330]  eta: 0:25:39  lr: 0.008300  loss: 0.5105 (0.5409)  loss_classifier: 0.2033 (0.2233)  loss_box_reg: 0.1818 (0.1918)  loss_objectness: 0.0530 (0.0588)  loss_rpn_box_reg: 0.0583 (0.0671)  time: 0.2178  data: 0.0067  max mem: 8983\n",
      "Epoch: [4]  [ 500/7330]  eta: 0:25:14  lr: 0.008238  loss: 0.5263 (0.5399)  loss_classifier: 0.2133 (0.2228)  loss_box_reg: 0.1851 (0.1917)  loss_objectness: 0.0533 (0.0587)  loss_rpn_box_reg: 0.0615 (0.0667)  time: 0.2244  data: 0.0064  max mem: 8983\n",
      "Epoch: [4]  [ 600/7330]  eta: 0:24:52  lr: 0.008176  loss: 0.5293 (0.5415)  loss_classifier: 0.2196 (0.2238)  loss_box_reg: 0.1917 (0.1926)  loss_objectness: 0.0589 (0.0587)  loss_rpn_box_reg: 0.0628 (0.0665)  time: 0.2192  data: 0.0064  max mem: 8983\n",
      "Epoch: [4]  [ 700/7330]  eta: 0:24:27  lr: 0.008114  loss: 0.5567 (0.5415)  loss_classifier: 0.2277 (0.2236)  loss_box_reg: 0.1915 (0.1926)  loss_objectness: 0.0560 (0.0588)  loss_rpn_box_reg: 0.0657 (0.0665)  time: 0.2214  data: 0.0068  max mem: 8983\n",
      "Epoch: [4]  [ 800/7330]  eta: 0:24:06  lr: 0.008053  loss: 0.5010 (0.5400)  loss_classifier: 0.2076 (0.2227)  loss_box_reg: 0.1911 (0.1923)  loss_objectness: 0.0557 (0.0586)  loss_rpn_box_reg: 0.0501 (0.0664)  time: 0.2222  data: 0.0065  max mem: 8983\n",
      "Epoch: [4]  [ 900/7330]  eta: 0:23:44  lr: 0.007991  loss: 0.5047 (0.5388)  loss_classifier: 0.2097 (0.2225)  loss_box_reg: 0.1788 (0.1918)  loss_objectness: 0.0509 (0.0584)  loss_rpn_box_reg: 0.0576 (0.0660)  time: 0.2217  data: 0.0064  max mem: 8983\n",
      "Epoch: [4]  [1000/7330]  eta: 0:23:21  lr: 0.007930  loss: 0.5501 (0.5394)  loss_classifier: 0.2255 (0.2228)  loss_box_reg: 0.1953 (0.1923)  loss_objectness: 0.0617 (0.0586)  loss_rpn_box_reg: 0.0633 (0.0658)  time: 0.2216  data: 0.0063  max mem: 8983\n",
      "Epoch: [4]  [1100/7330]  eta: 0:22:57  lr: 0.007868  loss: 0.5297 (0.5412)  loss_classifier: 0.2194 (0.2239)  loss_box_reg: 0.1902 (0.1931)  loss_objectness: 0.0574 (0.0586)  loss_rpn_box_reg: 0.0594 (0.0657)  time: 0.2204  data: 0.0063  max mem: 8983\n",
      "Epoch: [4]  [1200/7330]  eta: 0:22:35  lr: 0.007807  loss: 0.5758 (0.5412)  loss_classifier: 0.2363 (0.2239)  loss_box_reg: 0.2031 (0.1929)  loss_objectness: 0.0602 (0.0585)  loss_rpn_box_reg: 0.0676 (0.0658)  time: 0.2240  data: 0.0064  max mem: 8983\n",
      "Epoch: [4]  [1300/7330]  eta: 0:22:13  lr: 0.007746  loss: 0.4557 (0.5412)  loss_classifier: 0.1978 (0.2239)  loss_box_reg: 0.1848 (0.1930)  loss_objectness: 0.0512 (0.0586)  loss_rpn_box_reg: 0.0540 (0.0657)  time: 0.2241  data: 0.0063  max mem: 8983\n",
      "Epoch: [4]  [1400/7330]  eta: 0:21:51  lr: 0.007684  loss: 0.5453 (0.5420)  loss_classifier: 0.2165 (0.2243)  loss_box_reg: 0.1937 (0.1933)  loss_objectness: 0.0566 (0.0587)  loss_rpn_box_reg: 0.0711 (0.0657)  time: 0.2158  data: 0.0064  max mem: 8983\n",
      "Epoch: [4]  [1500/7330]  eta: 0:21:27  lr: 0.007623  loss: 0.5278 (0.5423)  loss_classifier: 0.2139 (0.2242)  loss_box_reg: 0.1917 (0.1935)  loss_objectness: 0.0550 (0.0588)  loss_rpn_box_reg: 0.0626 (0.0659)  time: 0.2216  data: 0.0064  max mem: 8983\n",
      "Epoch: [4]  [1600/7330]  eta: 0:21:05  lr: 0.007562  loss: 0.4826 (0.5413)  loss_classifier: 0.2084 (0.2239)  loss_box_reg: 0.1702 (0.1929)  loss_objectness: 0.0564 (0.0588)  loss_rpn_box_reg: 0.0558 (0.0657)  time: 0.2162  data: 0.0064  max mem: 8983\n",
      "Epoch: [4]  [1700/7330]  eta: 0:20:43  lr: 0.007502  loss: 0.5146 (0.5411)  loss_classifier: 0.2229 (0.2239)  loss_box_reg: 0.1883 (0.1930)  loss_objectness: 0.0531 (0.0587)  loss_rpn_box_reg: 0.0582 (0.0656)  time: 0.2204  data: 0.0069  max mem: 8983\n",
      "Epoch: [4]  [1800/7330]  eta: 0:20:20  lr: 0.007441  loss: 0.4857 (0.5403)  loss_classifier: 0.1910 (0.2236)  loss_box_reg: 0.1766 (0.1926)  loss_objectness: 0.0564 (0.0587)  loss_rpn_box_reg: 0.0538 (0.0654)  time: 0.2162  data: 0.0065  max mem: 8983\n",
      "Epoch: [4]  [1900/7330]  eta: 0:19:58  lr: 0.007380  loss: 0.5364 (0.5399)  loss_classifier: 0.2085 (0.2233)  loss_box_reg: 0.1781 (0.1925)  loss_objectness: 0.0577 (0.0587)  loss_rpn_box_reg: 0.0611 (0.0654)  time: 0.2231  data: 0.0062  max mem: 8983\n",
      "Epoch: [4]  [2000/7330]  eta: 0:19:36  lr: 0.007320  loss: 0.4914 (0.5396)  loss_classifier: 0.2065 (0.2231)  loss_box_reg: 0.1723 (0.1924)  loss_objectness: 0.0530 (0.0587)  loss_rpn_box_reg: 0.0566 (0.0653)  time: 0.2161  data: 0.0064  max mem: 8983\n",
      "Epoch: [4]  [2100/7330]  eta: 0:19:14  lr: 0.007259  loss: 0.4906 (0.5390)  loss_classifier: 0.2095 (0.2229)  loss_box_reg: 0.1753 (0.1922)  loss_objectness: 0.0534 (0.0586)  loss_rpn_box_reg: 0.0665 (0.0653)  time: 0.2186  data: 0.0065  max mem: 8983\n",
      "Epoch: [4]  [2200/7330]  eta: 0:18:51  lr: 0.007199  loss: 0.5535 (0.5399)  loss_classifier: 0.2190 (0.2234)  loss_box_reg: 0.1810 (0.1925)  loss_objectness: 0.0515 (0.0587)  loss_rpn_box_reg: 0.0625 (0.0653)  time: 0.2205  data: 0.0067  max mem: 8983\n",
      "Epoch: [4]  [2300/7330]  eta: 0:18:29  lr: 0.007138  loss: 0.4939 (0.5394)  loss_classifier: 0.2064 (0.2232)  loss_box_reg: 0.1820 (0.1924)  loss_objectness: 0.0530 (0.0586)  loss_rpn_box_reg: 0.0527 (0.0652)  time: 0.2184  data: 0.0065  max mem: 8983\n",
      "Epoch: [4]  [2400/7330]  eta: 0:18:07  lr: 0.007078  loss: 0.5065 (0.5398)  loss_classifier: 0.2076 (0.2234)  loss_box_reg: 0.1758 (0.1924)  loss_objectness: 0.0574 (0.0587)  loss_rpn_box_reg: 0.0649 (0.0654)  time: 0.2193  data: 0.0062  max mem: 8983\n",
      "Epoch: [4]  [2500/7330]  eta: 0:17:44  lr: 0.007018  loss: 0.5087 (0.5402)  loss_classifier: 0.2090 (0.2236)  loss_box_reg: 0.1821 (0.1924)  loss_objectness: 0.0512 (0.0588)  loss_rpn_box_reg: 0.0590 (0.0655)  time: 0.2214  data: 0.0069  max mem: 8983\n",
      "Epoch: [4]  [2600/7330]  eta: 0:17:22  lr: 0.006958  loss: 0.5287 (0.5401)  loss_classifier: 0.2139 (0.2235)  loss_box_reg: 0.1873 (0.1924)  loss_objectness: 0.0549 (0.0588)  loss_rpn_box_reg: 0.0612 (0.0654)  time: 0.2204  data: 0.0064  max mem: 8983\n",
      "Epoch: [4]  [2700/7330]  eta: 0:17:00  lr: 0.006899  loss: 0.5161 (0.5399)  loss_classifier: 0.2103 (0.2235)  loss_box_reg: 0.1818 (0.1922)  loss_objectness: 0.0551 (0.0588)  loss_rpn_box_reg: 0.0621 (0.0653)  time: 0.2225  data: 0.0065  max mem: 8983\n",
      "Epoch: [4]  [2800/7330]  eta: 0:16:37  lr: 0.006839  loss: 0.5290 (0.5397)  loss_classifier: 0.2224 (0.2234)  loss_box_reg: 0.1894 (0.1921)  loss_objectness: 0.0524 (0.0588)  loss_rpn_box_reg: 0.0619 (0.0653)  time: 0.2169  data: 0.0064  max mem: 8983\n",
      "Epoch: [4]  [2900/7330]  eta: 0:16:15  lr: 0.006779  loss: 0.5425 (0.5394)  loss_classifier: 0.2238 (0.2233)  loss_box_reg: 0.1972 (0.1921)  loss_objectness: 0.0575 (0.0588)  loss_rpn_box_reg: 0.0652 (0.0653)  time: 0.2227  data: 0.0063  max mem: 8983\n",
      "Epoch: [4]  [3000/7330]  eta: 0:15:53  lr: 0.006720  loss: 0.5032 (0.5395)  loss_classifier: 0.2088 (0.2234)  loss_box_reg: 0.1729 (0.1921)  loss_objectness: 0.0565 (0.0587)  loss_rpn_box_reg: 0.0537 (0.0653)  time: 0.2174  data: 0.0062  max mem: 8983\n",
      "Epoch: [4]  [3100/7330]  eta: 0:15:31  lr: 0.006661  loss: 0.4689 (0.5392)  loss_classifier: 0.2019 (0.2233)  loss_box_reg: 0.1699 (0.1921)  loss_objectness: 0.0533 (0.0587)  loss_rpn_box_reg: 0.0413 (0.0651)  time: 0.2214  data: 0.0061  max mem: 8983\n",
      "Epoch: [4]  [3200/7330]  eta: 0:15:09  lr: 0.006601  loss: 0.5548 (0.5389)  loss_classifier: 0.2073 (0.2231)  loss_box_reg: 0.1937 (0.1920)  loss_objectness: 0.0587 (0.0587)  loss_rpn_box_reg: 0.0702 (0.0651)  time: 0.2193  data: 0.0061  max mem: 8983\n",
      "Epoch: [4]  [3300/7330]  eta: 0:14:47  lr: 0.006542  loss: 0.5003 (0.5380)  loss_classifier: 0.2041 (0.2228)  loss_box_reg: 0.1816 (0.1918)  loss_objectness: 0.0536 (0.0585)  loss_rpn_box_reg: 0.0604 (0.0649)  time: 0.2259  data: 0.0066  max mem: 8983\n",
      "Epoch: [4]  [3400/7330]  eta: 0:14:25  lr: 0.006484  loss: 0.5276 (0.5379)  loss_classifier: 0.2156 (0.2228)  loss_box_reg: 0.1895 (0.1917)  loss_objectness: 0.0489 (0.0585)  loss_rpn_box_reg: 0.0656 (0.0649)  time: 0.2242  data: 0.0065  max mem: 8983\n",
      "Epoch: [4]  [3500/7330]  eta: 0:14:03  lr: 0.006425  loss: 0.5253 (0.5382)  loss_classifier: 0.2151 (0.2229)  loss_box_reg: 0.1751 (0.1918)  loss_objectness: 0.0469 (0.0586)  loss_rpn_box_reg: 0.0668 (0.0650)  time: 0.2193  data: 0.0063  max mem: 8983\n",
      "Epoch: [4]  [3600/7330]  eta: 0:13:40  lr: 0.006366  loss: 0.5094 (0.5376)  loss_classifier: 0.2198 (0.2226)  loss_box_reg: 0.1885 (0.1916)  loss_objectness: 0.0516 (0.0585)  loss_rpn_box_reg: 0.0577 (0.0649)  time: 0.2159  data: 0.0062  max mem: 8983\n",
      "Epoch: [4]  [3700/7330]  eta: 0:13:18  lr: 0.006308  loss: 0.4954 (0.5372)  loss_classifier: 0.2044 (0.2224)  loss_box_reg: 0.1854 (0.1914)  loss_objectness: 0.0556 (0.0585)  loss_rpn_box_reg: 0.0656 (0.0649)  time: 0.2195  data: 0.0064  max mem: 8983\n",
      "Epoch: [4]  [3800/7330]  eta: 0:12:56  lr: 0.006249  loss: 0.5480 (0.5369)  loss_classifier: 0.2181 (0.2223)  loss_box_reg: 0.1915 (0.1913)  loss_objectness: 0.0568 (0.0584)  loss_rpn_box_reg: 0.0695 (0.0648)  time: 0.2201  data: 0.0064  max mem: 8983\n",
      "Epoch: [4]  [3900/7330]  eta: 0:12:34  lr: 0.006191  loss: 0.4987 (0.5362)  loss_classifier: 0.2089 (0.2221)  loss_box_reg: 0.1787 (0.1911)  loss_objectness: 0.0531 (0.0583)  loss_rpn_box_reg: 0.0598 (0.0647)  time: 0.2173  data: 0.0063  max mem: 8983\n",
      "Epoch: [4]  [4000/7330]  eta: 0:12:12  lr: 0.006133  loss: 0.5317 (0.5363)  loss_classifier: 0.2092 (0.2222)  loss_box_reg: 0.1888 (0.1912)  loss_objectness: 0.0553 (0.0583)  loss_rpn_box_reg: 0.0568 (0.0646)  time: 0.2202  data: 0.0062  max mem: 8983\n",
      "Epoch: [4]  [4100/7330]  eta: 0:11:50  lr: 0.006075  loss: 0.5015 (0.5361)  loss_classifier: 0.2148 (0.2221)  loss_box_reg: 0.1809 (0.1911)  loss_objectness: 0.0522 (0.0583)  loss_rpn_box_reg: 0.0548 (0.0646)  time: 0.2264  data: 0.0068  max mem: 8983\n",
      "Epoch: [4]  [4200/7330]  eta: 0:11:28  lr: 0.006018  loss: 0.5117 (0.5361)  loss_classifier: 0.2193 (0.2221)  loss_box_reg: 0.1898 (0.1912)  loss_objectness: 0.0545 (0.0582)  loss_rpn_box_reg: 0.0566 (0.0645)  time: 0.2193  data: 0.0065  max mem: 8983\n",
      "Epoch: [4]  [4300/7330]  eta: 0:11:06  lr: 0.005960  loss: 0.4812 (0.5364)  loss_classifier: 0.2024 (0.2222)  loss_box_reg: 0.1647 (0.1912)  loss_objectness: 0.0564 (0.0582)  loss_rpn_box_reg: 0.0629 (0.0647)  time: 0.2184  data: 0.0061  max mem: 8983\n",
      "Epoch: [4]  [4400/7330]  eta: 0:10:44  lr: 0.005903  loss: 0.5698 (0.5368)  loss_classifier: 0.2418 (0.2224)  loss_box_reg: 0.2089 (0.1913)  loss_objectness: 0.0575 (0.0583)  loss_rpn_box_reg: 0.0531 (0.0647)  time: 0.2171  data: 0.0061  max mem: 8983\n",
      "Epoch: [4]  [4500/7330]  eta: 0:10:22  lr: 0.005846  loss: 0.5050 (0.5368)  loss_classifier: 0.2026 (0.2224)  loss_box_reg: 0.1766 (0.1913)  loss_objectness: 0.0556 (0.0583)  loss_rpn_box_reg: 0.0581 (0.0648)  time: 0.2185  data: 0.0062  max mem: 8983\n",
      "Epoch: [4]  [4600/7330]  eta: 0:10:00  lr: 0.005788  loss: 0.5407 (0.5368)  loss_classifier: 0.2273 (0.2224)  loss_box_reg: 0.1972 (0.1913)  loss_objectness: 0.0580 (0.0583)  loss_rpn_box_reg: 0.0485 (0.0648)  time: 0.2197  data: 0.0062  max mem: 8983\n",
      "Epoch: [4]  [4700/7330]  eta: 0:09:38  lr: 0.005732  loss: 0.5229 (0.5368)  loss_classifier: 0.2105 (0.2223)  loss_box_reg: 0.1911 (0.1913)  loss_objectness: 0.0520 (0.0583)  loss_rpn_box_reg: 0.0682 (0.0649)  time: 0.2229  data: 0.0061  max mem: 8983\n",
      "Epoch: [4]  [4800/7330]  eta: 0:09:16  lr: 0.005675  loss: 0.5567 (0.5366)  loss_classifier: 0.2233 (0.2222)  loss_box_reg: 0.2027 (0.1913)  loss_objectness: 0.0639 (0.0582)  loss_rpn_box_reg: 0.0628 (0.0649)  time: 0.2113  data: 0.0067  max mem: 8983\n",
      "Epoch: [4]  [4900/7330]  eta: 0:08:54  lr: 0.005618  loss: 0.5190 (0.5368)  loss_classifier: 0.2114 (0.2223)  loss_box_reg: 0.1885 (0.1913)  loss_objectness: 0.0573 (0.0583)  loss_rpn_box_reg: 0.0637 (0.0649)  time: 0.2195  data: 0.0066  max mem: 8983\n",
      "Epoch: [4]  [5000/7330]  eta: 0:08:32  lr: 0.005562  loss: 0.5938 (0.5370)  loss_classifier: 0.2558 (0.2223)  loss_box_reg: 0.2157 (0.1914)  loss_objectness: 0.0638 (0.0583)  loss_rpn_box_reg: 0.0646 (0.0649)  time: 0.2185  data: 0.0061  max mem: 8983\n",
      "Epoch: [4]  [5100/7330]  eta: 0:08:10  lr: 0.005506  loss: 0.5424 (0.5367)  loss_classifier: 0.2206 (0.2222)  loss_box_reg: 0.1861 (0.1914)  loss_objectness: 0.0591 (0.0582)  loss_rpn_box_reg: 0.0646 (0.0648)  time: 0.2215  data: 0.0063  max mem: 8983\n",
      "Epoch: [4]  [5200/7330]  eta: 0:07:48  lr: 0.005450  loss: 0.5539 (0.5368)  loss_classifier: 0.2323 (0.2223)  loss_box_reg: 0.1839 (0.1914)  loss_objectness: 0.0565 (0.0582)  loss_rpn_box_reg: 0.0627 (0.0648)  time: 0.2218  data: 0.0062  max mem: 8983\n",
      "Epoch: [4]  [5300/7330]  eta: 0:07:26  lr: 0.005394  loss: 0.5286 (0.5367)  loss_classifier: 0.2145 (0.2222)  loss_box_reg: 0.1778 (0.1914)  loss_objectness: 0.0643 (0.0582)  loss_rpn_box_reg: 0.0637 (0.0648)  time: 0.2200  data: 0.0061  max mem: 8983\n",
      "Epoch: [4]  [5400/7330]  eta: 0:07:04  lr: 0.005338  loss: 0.5405 (0.5364)  loss_classifier: 0.2115 (0.2221)  loss_box_reg: 0.1969 (0.1914)  loss_objectness: 0.0526 (0.0582)  loss_rpn_box_reg: 0.0649 (0.0648)  time: 0.2151  data: 0.0062  max mem: 8983\n",
      "Epoch: [4]  [5500/7330]  eta: 0:06:42  lr: 0.005283  loss: 0.5254 (0.5365)  loss_classifier: 0.2244 (0.2222)  loss_box_reg: 0.1803 (0.1914)  loss_objectness: 0.0521 (0.0582)  loss_rpn_box_reg: 0.0583 (0.0647)  time: 0.2260  data: 0.0063  max mem: 8983\n",
      "Epoch: [4]  [5600/7330]  eta: 0:06:20  lr: 0.005227  loss: 0.5479 (0.5365)  loss_classifier: 0.2235 (0.2222)  loss_box_reg: 0.1909 (0.1914)  loss_objectness: 0.0554 (0.0582)  loss_rpn_box_reg: 0.0566 (0.0648)  time: 0.2149  data: 0.0065  max mem: 8983\n",
      "Epoch: [4]  [5700/7330]  eta: 0:05:58  lr: 0.005172  loss: 0.5070 (0.5366)  loss_classifier: 0.2148 (0.2222)  loss_box_reg: 0.1817 (0.1914)  loss_objectness: 0.0540 (0.0582)  loss_rpn_box_reg: 0.0600 (0.0648)  time: 0.2235  data: 0.0065  max mem: 8983\n",
      "Epoch: [4]  [5800/7330]  eta: 0:05:36  lr: 0.005117  loss: 0.5194 (0.5365)  loss_classifier: 0.2106 (0.2222)  loss_box_reg: 0.1868 (0.1914)  loss_objectness: 0.0561 (0.0582)  loss_rpn_box_reg: 0.0571 (0.0647)  time: 0.2203  data: 0.0063  max mem: 8983\n",
      "Epoch: [4]  [5900/7330]  eta: 0:05:14  lr: 0.005063  loss: 0.5171 (0.5364)  loss_classifier: 0.2120 (0.2222)  loss_box_reg: 0.1935 (0.1914)  loss_objectness: 0.0528 (0.0581)  loss_rpn_box_reg: 0.0578 (0.0647)  time: 0.2164  data: 0.0062  max mem: 8983\n",
      "Epoch: [4]  [6000/7330]  eta: 0:04:52  lr: 0.005008  loss: 0.5719 (0.5363)  loss_classifier: 0.2300 (0.2221)  loss_box_reg: 0.1902 (0.1914)  loss_objectness: 0.0522 (0.0581)  loss_rpn_box_reg: 0.0641 (0.0647)  time: 0.2178  data: 0.0063  max mem: 8983\n",
      "Epoch: [4]  [6100/7330]  eta: 0:04:30  lr: 0.004954  loss: 0.5236 (0.5364)  loss_classifier: 0.2246 (0.2221)  loss_box_reg: 0.1837 (0.1915)  loss_objectness: 0.0571 (0.0581)  loss_rpn_box_reg: 0.0639 (0.0647)  time: 0.2156  data: 0.0062  max mem: 8983\n",
      "Epoch: [4]  [6200/7330]  eta: 0:04:08  lr: 0.004900  loss: 0.5129 (0.5363)  loss_classifier: 0.2143 (0.2221)  loss_box_reg: 0.1889 (0.1914)  loss_objectness: 0.0574 (0.0581)  loss_rpn_box_reg: 0.0518 (0.0647)  time: 0.2178  data: 0.0068  max mem: 8983\n",
      "Epoch: [4]  [6300/7330]  eta: 0:03:46  lr: 0.004846  loss: 0.4900 (0.5360)  loss_classifier: 0.2081 (0.2219)  loss_box_reg: 0.1713 (0.1913)  loss_objectness: 0.0534 (0.0580)  loss_rpn_box_reg: 0.0572 (0.0647)  time: 0.2193  data: 0.0063  max mem: 8983\n",
      "Epoch: [4]  [6400/7330]  eta: 0:03:24  lr: 0.004792  loss: 0.5739 (0.5363)  loss_classifier: 0.2381 (0.2220)  loss_box_reg: 0.2004 (0.1915)  loss_objectness: 0.0559 (0.0580)  loss_rpn_box_reg: 0.0616 (0.0647)  time: 0.2204  data: 0.0060  max mem: 8983\n",
      "Epoch: [4]  [6500/7330]  eta: 0:03:02  lr: 0.004738  loss: 0.5134 (0.5363)  loss_classifier: 0.2164 (0.2221)  loss_box_reg: 0.1807 (0.1915)  loss_objectness: 0.0509 (0.0580)  loss_rpn_box_reg: 0.0593 (0.0647)  time: 0.2194  data: 0.0064  max mem: 8983\n",
      "Epoch: [4]  [6600/7330]  eta: 0:02:40  lr: 0.004685  loss: 0.5241 (0.5365)  loss_classifier: 0.2202 (0.2222)  loss_box_reg: 0.1689 (0.1915)  loss_objectness: 0.0556 (0.0580)  loss_rpn_box_reg: 0.0626 (0.0647)  time: 0.2167  data: 0.0062  max mem: 8983\n",
      "Epoch: [4]  [6700/7330]  eta: 0:02:18  lr: 0.004632  loss: 0.4665 (0.5364)  loss_classifier: 0.1970 (0.2221)  loss_box_reg: 0.1787 (0.1915)  loss_objectness: 0.0527 (0.0580)  loss_rpn_box_reg: 0.0555 (0.0647)  time: 0.2151  data: 0.0063  max mem: 8983\n",
      "Epoch: [4]  [6800/7330]  eta: 0:01:56  lr: 0.004579  loss: 0.5284 (0.5364)  loss_classifier: 0.2156 (0.2221)  loss_box_reg: 0.1953 (0.1916)  loss_objectness: 0.0526 (0.0580)  loss_rpn_box_reg: 0.0550 (0.0647)  time: 0.2194  data: 0.0063  max mem: 8983\n",
      "Epoch: [4]  [6900/7330]  eta: 0:01:34  lr: 0.004526  loss: 0.5195 (0.5363)  loss_classifier: 0.2184 (0.2221)  loss_box_reg: 0.1822 (0.1915)  loss_objectness: 0.0507 (0.0580)  loss_rpn_box_reg: 0.0540 (0.0647)  time: 0.2240  data: 0.0064  max mem: 8983\n",
      "Epoch: [4]  [7000/7330]  eta: 0:01:12  lr: 0.004474  loss: 0.5560 (0.5362)  loss_classifier: 0.2367 (0.2220)  loss_box_reg: 0.1900 (0.1915)  loss_objectness: 0.0512 (0.0580)  loss_rpn_box_reg: 0.0553 (0.0647)  time: 0.2255  data: 0.0064  max mem: 8983\n",
      "Epoch: [4]  [7100/7330]  eta: 0:00:50  lr: 0.004422  loss: 0.5211 (0.5362)  loss_classifier: 0.2094 (0.2220)  loss_box_reg: 0.1926 (0.1916)  loss_objectness: 0.0487 (0.0579)  loss_rpn_box_reg: 0.0503 (0.0647)  time: 0.2232  data: 0.0063  max mem: 8983\n",
      "Epoch: [4]  [7200/7330]  eta: 0:00:28  lr: 0.004370  loss: 0.6167 (0.5363)  loss_classifier: 0.2363 (0.2221)  loss_box_reg: 0.2155 (0.1916)  loss_objectness: 0.0595 (0.0579)  loss_rpn_box_reg: 0.0637 (0.0647)  time: 0.2208  data: 0.0067  max mem: 8983\n",
      "Epoch: [4]  [7300/7330]  eta: 0:00:06  lr: 0.004318  loss: 0.5659 (0.5364)  loss_classifier: 0.2274 (0.2221)  loss_box_reg: 0.1858 (0.1916)  loss_objectness: 0.0617 (0.0580)  loss_rpn_box_reg: 0.0616 (0.0647)  time: 0.2243  data: 0.0067  max mem: 8983\n",
      "Epoch: [4]  [7329/7330]  eta: 0:00:00  lr: 0.004303  loss: 0.5257 (0.5364)  loss_classifier: 0.2142 (0.2221)  loss_box_reg: 0.1794 (0.1917)  loss_objectness: 0.0558 (0.0580)  loss_rpn_box_reg: 0.0565 (0.0647)  time: 0.2128  data: 0.0061  max mem: 8983\n",
      "Epoch: [4] Total time: 0:26:50 (0.2198 s / it)\n",
      "Test:  [  0/313]  eta: 0:02:39  model_time: 0.1373 (0.1373)  evaluator_time: 0.0266 (0.0266)  time: 0.5106  data: 0.3376  max mem: 8983\n",
      "Test:  [312/313]  eta: 0:00:00  model_time: 0.1287 (0.1301)  evaluator_time: 0.0181 (0.0226)  time: 0.1578  data: 0.0062  max mem: 8983\n",
      "Test: Total time: 0:00:53 (0.1695 s / it)\n",
      "Averaged stats: model_time: 0.1287 (0.1301)  evaluator_time: 0.0181 (0.0226)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.417\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.694\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.459\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.252\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.430\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.598\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.343\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.404\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.426\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.278\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.430\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.600\n",
      "Epoch: [5]  [   0/7330]  eta: 1:42:17  lr: 0.004302  loss: 0.4329 (0.4329)  loss_classifier: 0.1825 (0.1825)  loss_box_reg: 0.1609 (0.1609)  loss_objectness: 0.0438 (0.0438)  loss_rpn_box_reg: 0.0457 (0.0457)  time: 0.8374  data: 0.6046  max mem: 8983\n",
      "Epoch: [5]  [ 100/7330]  eta: 0:27:21  lr: 0.004251  loss: 0.4769 (0.5137)  loss_classifier: 0.1896 (0.2102)  loss_box_reg: 0.1728 (0.1874)  loss_objectness: 0.0501 (0.0541)  loss_rpn_box_reg: 0.0590 (0.0620)  time: 0.2175  data: 0.0067  max mem: 8983\n",
      "Epoch: [5]  [ 200/7330]  eta: 0:26:40  lr: 0.004199  loss: 0.5194 (0.5189)  loss_classifier: 0.2063 (0.2131)  loss_box_reg: 0.1888 (0.1895)  loss_objectness: 0.0553 (0.0547)  loss_rpn_box_reg: 0.0651 (0.0616)  time: 0.2215  data: 0.0067  max mem: 8983\n",
      "Epoch: [5]  [ 300/7330]  eta: 0:26:03  lr: 0.004148  loss: 0.5344 (0.5214)  loss_classifier: 0.2366 (0.2146)  loss_box_reg: 0.1894 (0.1904)  loss_objectness: 0.0516 (0.0545)  loss_rpn_box_reg: 0.0556 (0.0620)  time: 0.2129  data: 0.0067  max mem: 8983\n",
      "Epoch: [5]  [ 400/7330]  eta: 0:25:35  lr: 0.004098  loss: 0.5328 (0.5217)  loss_classifier: 0.2151 (0.2141)  loss_box_reg: 0.1966 (0.1903)  loss_objectness: 0.0532 (0.0553)  loss_rpn_box_reg: 0.0638 (0.0619)  time: 0.2182  data: 0.0067  max mem: 8983\n",
      "Epoch: [5]  [ 500/7330]  eta: 0:25:11  lr: 0.004047  loss: 0.4721 (0.5213)  loss_classifier: 0.2075 (0.2140)  loss_box_reg: 0.1819 (0.1905)  loss_objectness: 0.0460 (0.0548)  loss_rpn_box_reg: 0.0539 (0.0620)  time: 0.2230  data: 0.0064  max mem: 8983\n",
      "Epoch: [5]  [ 600/7330]  eta: 0:24:46  lr: 0.003997  loss: 0.5256 (0.5196)  loss_classifier: 0.2186 (0.2134)  loss_box_reg: 0.1917 (0.1896)  loss_objectness: 0.0578 (0.0548)  loss_rpn_box_reg: 0.0569 (0.0619)  time: 0.2193  data: 0.0064  max mem: 8983\n",
      "Epoch: [5]  [ 700/7330]  eta: 0:24:22  lr: 0.003946  loss: 0.4536 (0.5193)  loss_classifier: 0.1992 (0.2131)  loss_box_reg: 0.1725 (0.1892)  loss_objectness: 0.0502 (0.0549)  loss_rpn_box_reg: 0.0467 (0.0621)  time: 0.2230  data: 0.0064  max mem: 8983\n",
      "Epoch: [5]  [ 800/7330]  eta: 0:23:59  lr: 0.003897  loss: 0.4778 (0.5170)  loss_classifier: 0.2001 (0.2123)  loss_box_reg: 0.1762 (0.1883)  loss_objectness: 0.0482 (0.0545)  loss_rpn_box_reg: 0.0539 (0.0620)  time: 0.2201  data: 0.0065  max mem: 8983\n",
      "Epoch: [5]  [ 900/7330]  eta: 0:23:36  lr: 0.003847  loss: 0.5168 (0.5166)  loss_classifier: 0.2209 (0.2123)  loss_box_reg: 0.1985 (0.1877)  loss_objectness: 0.0546 (0.0544)  loss_rpn_box_reg: 0.0620 (0.0622)  time: 0.2179  data: 0.0068  max mem: 8983\n",
      "Epoch: [5]  [1000/7330]  eta: 0:23:14  lr: 0.003798  loss: 0.5502 (0.5182)  loss_classifier: 0.2288 (0.2132)  loss_box_reg: 0.1999 (0.1880)  loss_objectness: 0.0586 (0.0546)  loss_rpn_box_reg: 0.0639 (0.0625)  time: 0.2156  data: 0.0063  max mem: 8983\n",
      "Epoch: [5]  [1100/7330]  eta: 0:22:52  lr: 0.003748  loss: 0.5007 (0.5182)  loss_classifier: 0.2023 (0.2133)  loss_box_reg: 0.1734 (0.1879)  loss_objectness: 0.0527 (0.0546)  loss_rpn_box_reg: 0.0564 (0.0624)  time: 0.2176  data: 0.0065  max mem: 8983\n",
      "Epoch: [5]  [1200/7330]  eta: 0:22:29  lr: 0.003699  loss: 0.5347 (0.5184)  loss_classifier: 0.2125 (0.2133)  loss_box_reg: 0.1930 (0.1880)  loss_objectness: 0.0472 (0.0546)  loss_rpn_box_reg: 0.0503 (0.0624)  time: 0.2170  data: 0.0065  max mem: 8983\n",
      "Epoch: [5]  [1300/7330]  eta: 0:22:06  lr: 0.003651  loss: 0.5100 (0.5185)  loss_classifier: 0.2095 (0.2132)  loss_box_reg: 0.1800 (0.1880)  loss_objectness: 0.0531 (0.0548)  loss_rpn_box_reg: 0.0567 (0.0625)  time: 0.2153  data: 0.0064  max mem: 8983\n",
      "Epoch: [5]  [1400/7330]  eta: 0:21:43  lr: 0.003602  loss: 0.5293 (0.5190)  loss_classifier: 0.2099 (0.2133)  loss_box_reg: 0.2079 (0.1882)  loss_objectness: 0.0560 (0.0549)  loss_rpn_box_reg: 0.0545 (0.0627)  time: 0.2142  data: 0.0068  max mem: 8983\n",
      "Epoch: [5]  [1500/7330]  eta: 0:21:21  lr: 0.003554  loss: 0.5120 (0.5183)  loss_classifier: 0.1992 (0.2130)  loss_box_reg: 0.1824 (0.1876)  loss_objectness: 0.0573 (0.0549)  loss_rpn_box_reg: 0.0678 (0.0628)  time: 0.2224  data: 0.0068  max mem: 8983\n",
      "Epoch: [5]  [1600/7330]  eta: 0:20:59  lr: 0.003506  loss: 0.5113 (0.5181)  loss_classifier: 0.2124 (0.2128)  loss_box_reg: 0.1799 (0.1876)  loss_objectness: 0.0537 (0.0548)  loss_rpn_box_reg: 0.0623 (0.0630)  time: 0.2199  data: 0.0064  max mem: 8983\n",
      "Epoch: [5]  [1700/7330]  eta: 0:20:38  lr: 0.003459  loss: 0.5322 (0.5176)  loss_classifier: 0.2098 (0.2126)  loss_box_reg: 0.1895 (0.1873)  loss_objectness: 0.0510 (0.0547)  loss_rpn_box_reg: 0.0677 (0.0629)  time: 0.2237  data: 0.0066  max mem: 8983\n",
      "Epoch: [5]  [1800/7330]  eta: 0:20:14  lr: 0.003411  loss: 0.5178 (0.5165)  loss_classifier: 0.2078 (0.2121)  loss_box_reg: 0.1838 (0.1868)  loss_objectness: 0.0554 (0.0546)  loss_rpn_box_reg: 0.0635 (0.0629)  time: 0.2127  data: 0.0066  max mem: 8983\n",
      "Epoch: [5]  [1900/7330]  eta: 0:19:52  lr: 0.003364  loss: 0.4868 (0.5167)  loss_classifier: 0.1941 (0.2123)  loss_box_reg: 0.1828 (0.1869)  loss_objectness: 0.0509 (0.0547)  loss_rpn_box_reg: 0.0657 (0.0629)  time: 0.2220  data: 0.0064  max mem: 8983\n",
      "Epoch: [5]  [2000/7330]  eta: 0:19:30  lr: 0.003317  loss: 0.5352 (0.5172)  loss_classifier: 0.2136 (0.2126)  loss_box_reg: 0.1909 (0.1871)  loss_objectness: 0.0550 (0.0547)  loss_rpn_box_reg: 0.0467 (0.0629)  time: 0.2214  data: 0.0065  max mem: 8983\n",
      "Epoch: [5]  [2100/7330]  eta: 0:19:08  lr: 0.003271  loss: 0.4540 (0.5167)  loss_classifier: 0.1906 (0.2124)  loss_box_reg: 0.1777 (0.1870)  loss_objectness: 0.0547 (0.0546)  loss_rpn_box_reg: 0.0558 (0.0627)  time: 0.2230  data: 0.0066  max mem: 8983\n",
      "Epoch: [5]  [2200/7330]  eta: 0:18:47  lr: 0.003224  loss: 0.5183 (0.5174)  loss_classifier: 0.2120 (0.2127)  loss_box_reg: 0.1735 (0.1871)  loss_objectness: 0.0559 (0.0547)  loss_rpn_box_reg: 0.0704 (0.0630)  time: 0.2261  data: 0.0066  max mem: 8983\n",
      "Epoch: [5]  [2300/7330]  eta: 0:18:25  lr: 0.003178  loss: 0.5215 (0.5177)  loss_classifier: 0.2192 (0.2129)  loss_box_reg: 0.1864 (0.1873)  loss_objectness: 0.0505 (0.0546)  loss_rpn_box_reg: 0.0602 (0.0630)  time: 0.2265  data: 0.0065  max mem: 8983\n",
      "Epoch: [5]  [2400/7330]  eta: 0:18:03  lr: 0.003133  loss: 0.5186 (0.5175)  loss_classifier: 0.2142 (0.2127)  loss_box_reg: 0.1930 (0.1873)  loss_objectness: 0.0553 (0.0545)  loss_rpn_box_reg: 0.0523 (0.0629)  time: 0.2237  data: 0.0064  max mem: 8983\n",
      "Epoch: [5]  [2500/7330]  eta: 0:17:41  lr: 0.003087  loss: 0.5243 (0.5178)  loss_classifier: 0.2122 (0.2128)  loss_box_reg: 0.1961 (0.1875)  loss_objectness: 0.0475 (0.0546)  loss_rpn_box_reg: 0.0653 (0.0630)  time: 0.2253  data: 0.0063  max mem: 8983\n",
      "Epoch: [5]  [2600/7330]  eta: 0:17:19  lr: 0.003042  loss: 0.5392 (0.5180)  loss_classifier: 0.2190 (0.2128)  loss_box_reg: 0.1868 (0.1877)  loss_objectness: 0.0546 (0.0546)  loss_rpn_box_reg: 0.0582 (0.0628)  time: 0.2176  data: 0.0064  max mem: 8983\n",
      "Epoch: [5]  [2700/7330]  eta: 0:16:57  lr: 0.002997  loss: 0.5238 (0.5180)  loss_classifier: 0.2184 (0.2129)  loss_box_reg: 0.1896 (0.1877)  loss_objectness: 0.0492 (0.0546)  loss_rpn_box_reg: 0.0613 (0.0629)  time: 0.2187  data: 0.0062  max mem: 8983\n",
      "Epoch: [5]  [2800/7330]  eta: 0:16:35  lr: 0.002952  loss: 0.5248 (0.5180)  loss_classifier: 0.1908 (0.2128)  loss_box_reg: 0.1805 (0.1877)  loss_objectness: 0.0535 (0.0545)  loss_rpn_box_reg: 0.0606 (0.0629)  time: 0.2108  data: 0.0064  max mem: 8983\n",
      "Epoch: [5]  [2900/7330]  eta: 0:16:13  lr: 0.002908  loss: 0.5889 (0.5183)  loss_classifier: 0.2352 (0.2130)  loss_box_reg: 0.2016 (0.1879)  loss_objectness: 0.0578 (0.0546)  loss_rpn_box_reg: 0.0649 (0.0629)  time: 0.2171  data: 0.0062  max mem: 8983\n",
      "Epoch: [5]  [3000/7330]  eta: 0:15:51  lr: 0.002863  loss: 0.5304 (0.5179)  loss_classifier: 0.2274 (0.2128)  loss_box_reg: 0.1909 (0.1877)  loss_objectness: 0.0520 (0.0546)  loss_rpn_box_reg: 0.0556 (0.0628)  time: 0.2184  data: 0.0065  max mem: 8983\n",
      "Epoch: [5]  [3100/7330]  eta: 0:15:28  lr: 0.002820  loss: 0.5811 (0.5184)  loss_classifier: 0.2355 (0.2129)  loss_box_reg: 0.1883 (0.1880)  loss_objectness: 0.0551 (0.0546)  loss_rpn_box_reg: 0.0621 (0.0628)  time: 0.2159  data: 0.0062  max mem: 8983\n",
      "Epoch: [5]  [3200/7330]  eta: 0:15:06  lr: 0.002776  loss: 0.5453 (0.5186)  loss_classifier: 0.2245 (0.2130)  loss_box_reg: 0.2089 (0.1881)  loss_objectness: 0.0499 (0.0546)  loss_rpn_box_reg: 0.0619 (0.0628)  time: 0.2183  data: 0.0064  max mem: 8983\n",
      "Epoch: [5]  [3300/7330]  eta: 0:14:44  lr: 0.002733  loss: 0.5310 (0.5185)  loss_classifier: 0.2222 (0.2130)  loss_box_reg: 0.1937 (0.1881)  loss_objectness: 0.0524 (0.0546)  loss_rpn_box_reg: 0.0674 (0.0628)  time: 0.2176  data: 0.0063  max mem: 8983\n",
      "Epoch: [5]  [3400/7330]  eta: 0:14:22  lr: 0.002690  loss: 0.5515 (0.5186)  loss_classifier: 0.2280 (0.2130)  loss_box_reg: 0.1986 (0.1881)  loss_objectness: 0.0510 (0.0546)  loss_rpn_box_reg: 0.0658 (0.0628)  time: 0.2162  data: 0.0063  max mem: 8983\n",
      "Epoch: [5]  [3500/7330]  eta: 0:14:00  lr: 0.002647  loss: 0.5155 (0.5187)  loss_classifier: 0.2073 (0.2131)  loss_box_reg: 0.1896 (0.1882)  loss_objectness: 0.0507 (0.0546)  loss_rpn_box_reg: 0.0624 (0.0628)  time: 0.2176  data: 0.0064  max mem: 8983\n",
      "Epoch: [5]  [3600/7330]  eta: 0:13:38  lr: 0.002605  loss: 0.5341 (0.5192)  loss_classifier: 0.2125 (0.2133)  loss_box_reg: 0.2035 (0.1884)  loss_objectness: 0.0542 (0.0547)  loss_rpn_box_reg: 0.0615 (0.0629)  time: 0.2188  data: 0.0062  max mem: 8983\n",
      "Epoch: [5]  [3700/7330]  eta: 0:13:16  lr: 0.002562  loss: 0.4980 (0.5192)  loss_classifier: 0.2135 (0.2134)  loss_box_reg: 0.1863 (0.1884)  loss_objectness: 0.0466 (0.0546)  loss_rpn_box_reg: 0.0534 (0.0628)  time: 0.2220  data: 0.0062  max mem: 8983\n",
      "Epoch: [5]  [3800/7330]  eta: 0:12:54  lr: 0.002521  loss: 0.4907 (0.5193)  loss_classifier: 0.2025 (0.2134)  loss_box_reg: 0.1770 (0.1885)  loss_objectness: 0.0513 (0.0547)  loss_rpn_box_reg: 0.0563 (0.0628)  time: 0.2122  data: 0.0064  max mem: 8983\n",
      "Epoch: [5]  [3900/7330]  eta: 0:12:32  lr: 0.002479  loss: 0.5112 (0.5193)  loss_classifier: 0.2149 (0.2134)  loss_box_reg: 0.1881 (0.1885)  loss_objectness: 0.0492 (0.0546)  loss_rpn_box_reg: 0.0693 (0.0628)  time: 0.2173  data: 0.0064  max mem: 8983\n",
      "Epoch: [5]  [4000/7330]  eta: 0:12:10  lr: 0.002438  loss: 0.5391 (0.5192)  loss_classifier: 0.2050 (0.2133)  loss_box_reg: 0.2013 (0.1885)  loss_objectness: 0.0506 (0.0546)  loss_rpn_box_reg: 0.0727 (0.0628)  time: 0.2171  data: 0.0061  max mem: 8983\n",
      "Epoch: [5]  [4100/7330]  eta: 0:11:48  lr: 0.002397  loss: 0.5080 (0.5190)  loss_classifier: 0.2218 (0.2132)  loss_box_reg: 0.1814 (0.1885)  loss_objectness: 0.0559 (0.0546)  loss_rpn_box_reg: 0.0572 (0.0627)  time: 0.2247  data: 0.0061  max mem: 8983\n",
      "Epoch: [5]  [4200/7330]  eta: 0:11:26  lr: 0.002356  loss: 0.5059 (0.5188)  loss_classifier: 0.2079 (0.2132)  loss_box_reg: 0.1759 (0.1885)  loss_objectness: 0.0484 (0.0546)  loss_rpn_box_reg: 0.0648 (0.0627)  time: 0.2216  data: 0.0063  max mem: 8983\n",
      "Epoch: [5]  [4300/7330]  eta: 0:11:04  lr: 0.002316  loss: 0.5155 (0.5187)  loss_classifier: 0.2119 (0.2131)  loss_box_reg: 0.1750 (0.1884)  loss_objectness: 0.0511 (0.0545)  loss_rpn_box_reg: 0.0596 (0.0626)  time: 0.2213  data: 0.0061  max mem: 8983\n",
      "Epoch: [5]  [4400/7330]  eta: 0:10:43  lr: 0.002276  loss: 0.5153 (0.5189)  loss_classifier: 0.2248 (0.2132)  loss_box_reg: 0.1984 (0.1885)  loss_objectness: 0.0530 (0.0545)  loss_rpn_box_reg: 0.0555 (0.0627)  time: 0.2264  data: 0.0063  max mem: 8983\n",
      "Epoch: [5]  [4500/7330]  eta: 0:10:21  lr: 0.002236  loss: 0.5078 (0.5187)  loss_classifier: 0.2146 (0.2131)  loss_box_reg: 0.1844 (0.1885)  loss_objectness: 0.0576 (0.0545)  loss_rpn_box_reg: 0.0504 (0.0626)  time: 0.2130  data: 0.0063  max mem: 8983\n",
      "Epoch: [5]  [4600/7330]  eta: 0:09:59  lr: 0.002197  loss: 0.5359 (0.5191)  loss_classifier: 0.2200 (0.2132)  loss_box_reg: 0.1882 (0.1887)  loss_objectness: 0.0586 (0.0546)  loss_rpn_box_reg: 0.0601 (0.0626)  time: 0.2237  data: 0.0063  max mem: 8983\n",
      "Epoch: [5]  [4700/7330]  eta: 0:09:37  lr: 0.002157  loss: 0.5132 (0.5191)  loss_classifier: 0.2132 (0.2133)  loss_box_reg: 0.1973 (0.1887)  loss_objectness: 0.0462 (0.0546)  loss_rpn_box_reg: 0.0587 (0.0625)  time: 0.2219  data: 0.0063  max mem: 8983\n",
      "Epoch: [5]  [4800/7330]  eta: 0:09:15  lr: 0.002119  loss: 0.5234 (0.5190)  loss_classifier: 0.2098 (0.2132)  loss_box_reg: 0.1848 (0.1887)  loss_objectness: 0.0546 (0.0545)  loss_rpn_box_reg: 0.0577 (0.0625)  time: 0.2184  data: 0.0062  max mem: 8983\n",
      "Epoch: [5]  [4900/7330]  eta: 0:08:53  lr: 0.002080  loss: 0.4894 (0.5191)  loss_classifier: 0.2039 (0.2132)  loss_box_reg: 0.1894 (0.1888)  loss_objectness: 0.0496 (0.0546)  loss_rpn_box_reg: 0.0556 (0.0625)  time: 0.2177  data: 0.0067  max mem: 8983\n",
      "Epoch: [5]  [5000/7330]  eta: 0:08:31  lr: 0.002042  loss: 0.4969 (0.5190)  loss_classifier: 0.2075 (0.2132)  loss_box_reg: 0.1863 (0.1887)  loss_objectness: 0.0490 (0.0546)  loss_rpn_box_reg: 0.0517 (0.0625)  time: 0.2206  data: 0.0065  max mem: 8983\n",
      "Epoch: [5]  [5100/7330]  eta: 0:08:09  lr: 0.002004  loss: 0.4906 (0.5186)  loss_classifier: 0.2068 (0.2131)  loss_box_reg: 0.1755 (0.1886)  loss_objectness: 0.0481 (0.0546)  loss_rpn_box_reg: 0.0576 (0.0624)  time: 0.2221  data: 0.0063  max mem: 8983\n",
      "Epoch: [5]  [5200/7330]  eta: 0:07:47  lr: 0.001966  loss: 0.4741 (0.5183)  loss_classifier: 0.1853 (0.2129)  loss_box_reg: 0.1740 (0.1885)  loss_objectness: 0.0513 (0.0546)  loss_rpn_box_reg: 0.0612 (0.0624)  time: 0.2192  data: 0.0062  max mem: 8983\n",
      "Epoch: [5]  [5300/7330]  eta: 0:07:25  lr: 0.001929  loss: 0.4752 (0.5181)  loss_classifier: 0.1915 (0.2128)  loss_box_reg: 0.1687 (0.1884)  loss_objectness: 0.0504 (0.0546)  loss_rpn_box_reg: 0.0482 (0.0623)  time: 0.2182  data: 0.0063  max mem: 8983\n",
      "Epoch: [5]  [5400/7330]  eta: 0:07:03  lr: 0.001892  loss: 0.4669 (0.5178)  loss_classifier: 0.1914 (0.2126)  loss_box_reg: 0.1765 (0.1883)  loss_objectness: 0.0499 (0.0545)  loss_rpn_box_reg: 0.0608 (0.0623)  time: 0.2181  data: 0.0065  max mem: 8983\n",
      "Epoch: [5]  [5500/7330]  eta: 0:06:41  lr: 0.001856  loss: 0.4703 (0.5176)  loss_classifier: 0.1952 (0.2125)  loss_box_reg: 0.1700 (0.1883)  loss_objectness: 0.0470 (0.0545)  loss_rpn_box_reg: 0.0568 (0.0624)  time: 0.2226  data: 0.0063  max mem: 8983\n",
      "Epoch: [5]  [5600/7330]  eta: 0:06:19  lr: 0.001819  loss: 0.5274 (0.5176)  loss_classifier: 0.2150 (0.2125)  loss_box_reg: 0.1942 (0.1882)  loss_objectness: 0.0497 (0.0545)  loss_rpn_box_reg: 0.0543 (0.0624)  time: 0.2213  data: 0.0062  max mem: 8983\n",
      "Epoch: [5]  [5700/7330]  eta: 0:05:57  lr: 0.001783  loss: 0.4973 (0.5174)  loss_classifier: 0.1938 (0.2124)  loss_box_reg: 0.1823 (0.1882)  loss_objectness: 0.0532 (0.0545)  loss_rpn_box_reg: 0.0595 (0.0624)  time: 0.2175  data: 0.0063  max mem: 8983\n",
      "Epoch: [5]  [5800/7330]  eta: 0:05:35  lr: 0.001748  loss: 0.4764 (0.5172)  loss_classifier: 0.1972 (0.2123)  loss_box_reg: 0.1705 (0.1881)  loss_objectness: 0.0413 (0.0545)  loss_rpn_box_reg: 0.0552 (0.0623)  time: 0.2189  data: 0.0062  max mem: 8983\n",
      "Epoch: [5]  [5900/7330]  eta: 0:05:13  lr: 0.001712  loss: 0.4917 (0.5171)  loss_classifier: 0.1858 (0.2122)  loss_box_reg: 0.1758 (0.1880)  loss_objectness: 0.0455 (0.0545)  loss_rpn_box_reg: 0.0521 (0.0624)  time: 0.2211  data: 0.0063  max mem: 8983\n",
      "Epoch: [5]  [6000/7330]  eta: 0:04:51  lr: 0.001678  loss: 0.4708 (0.5170)  loss_classifier: 0.1921 (0.2122)  loss_box_reg: 0.1741 (0.1880)  loss_objectness: 0.0497 (0.0544)  loss_rpn_box_reg: 0.0528 (0.0623)  time: 0.2216  data: 0.0062  max mem: 8983\n",
      "Epoch: [5]  [6100/7330]  eta: 0:04:29  lr: 0.001643  loss: 0.5090 (0.5170)  loss_classifier: 0.2016 (0.2122)  loss_box_reg: 0.1844 (0.1881)  loss_objectness: 0.0458 (0.0544)  loss_rpn_box_reg: 0.0566 (0.0622)  time: 0.2183  data: 0.0065  max mem: 8983\n",
      "Epoch: [5]  [6200/7330]  eta: 0:04:07  lr: 0.001609  loss: 0.4913 (0.5169)  loss_classifier: 0.1996 (0.2122)  loss_box_reg: 0.1836 (0.1881)  loss_objectness: 0.0504 (0.0544)  loss_rpn_box_reg: 0.0480 (0.0622)  time: 0.2260  data: 0.0067  max mem: 8983\n",
      "Epoch: [5]  [6300/7330]  eta: 0:03:46  lr: 0.001575  loss: 0.4795 (0.5170)  loss_classifier: 0.2044 (0.2121)  loss_box_reg: 0.1776 (0.1882)  loss_objectness: 0.0523 (0.0545)  loss_rpn_box_reg: 0.0549 (0.0622)  time: 0.2207  data: 0.0063  max mem: 8983\n",
      "Epoch: [5]  [6400/7330]  eta: 0:03:24  lr: 0.001541  loss: 0.5285 (0.5169)  loss_classifier: 0.2174 (0.2121)  loss_box_reg: 0.2061 (0.1881)  loss_objectness: 0.0580 (0.0545)  loss_rpn_box_reg: 0.0600 (0.0622)  time: 0.2200  data: 0.0067  max mem: 8983\n",
      "Epoch: [5]  [6500/7330]  eta: 0:03:02  lr: 0.001508  loss: 0.5159 (0.5169)  loss_classifier: 0.2010 (0.2121)  loss_box_reg: 0.1724 (0.1882)  loss_objectness: 0.0515 (0.0545)  loss_rpn_box_reg: 0.0524 (0.0621)  time: 0.2257  data: 0.0064  max mem: 8983\n",
      "Epoch: [5]  [6600/7330]  eta: 0:02:40  lr: 0.001475  loss: 0.4655 (0.5168)  loss_classifier: 0.1949 (0.2121)  loss_box_reg: 0.1795 (0.1882)  loss_objectness: 0.0491 (0.0544)  loss_rpn_box_reg: 0.0443 (0.0621)  time: 0.2202  data: 0.0063  max mem: 8983\n",
      "Epoch: [5]  [6700/7330]  eta: 0:02:18  lr: 0.001442  loss: 0.5085 (0.5167)  loss_classifier: 0.2070 (0.2120)  loss_box_reg: 0.1975 (0.1881)  loss_objectness: 0.0484 (0.0544)  loss_rpn_box_reg: 0.0596 (0.0622)  time: 0.2221  data: 0.0061  max mem: 8983\n",
      "Epoch: [5]  [6800/7330]  eta: 0:01:56  lr: 0.001410  loss: 0.5132 (0.5166)  loss_classifier: 0.2024 (0.2119)  loss_box_reg: 0.1765 (0.1881)  loss_objectness: 0.0550 (0.0544)  loss_rpn_box_reg: 0.0603 (0.0622)  time: 0.2227  data: 0.0061  max mem: 8983\n",
      "Epoch: [5]  [6900/7330]  eta: 0:01:34  lr: 0.001378  loss: 0.5033 (0.5169)  loss_classifier: 0.2048 (0.2121)  loss_box_reg: 0.1872 (0.1882)  loss_objectness: 0.0594 (0.0545)  loss_rpn_box_reg: 0.0646 (0.0622)  time: 0.2198  data: 0.0066  max mem: 8983\n",
      "Epoch: [5]  [7000/7330]  eta: 0:01:12  lr: 0.001346  loss: 0.4921 (0.5168)  loss_classifier: 0.2093 (0.2121)  loss_box_reg: 0.1794 (0.1881)  loss_objectness: 0.0483 (0.0545)  loss_rpn_box_reg: 0.0593 (0.0622)  time: 0.2221  data: 0.0062  max mem: 8983\n",
      "Epoch: [5]  [7100/7330]  eta: 0:00:50  lr: 0.001315  loss: 0.4927 (0.5168)  loss_classifier: 0.2031 (0.2120)  loss_box_reg: 0.1819 (0.1881)  loss_objectness: 0.0505 (0.0544)  loss_rpn_box_reg: 0.0617 (0.0622)  time: 0.2205  data: 0.0063  max mem: 8983\n",
      "Epoch: [5]  [7200/7330]  eta: 0:00:28  lr: 0.001284  loss: 0.4885 (0.5167)  loss_classifier: 0.1941 (0.2120)  loss_box_reg: 0.1799 (0.1881)  loss_objectness: 0.0509 (0.0544)  loss_rpn_box_reg: 0.0586 (0.0621)  time: 0.2224  data: 0.0063  max mem: 8983\n",
      "Epoch: [5]  [7300/7330]  eta: 0:00:06  lr: 0.001253  loss: 0.4805 (0.5165)  loss_classifier: 0.1926 (0.2119)  loss_box_reg: 0.1713 (0.1881)  loss_objectness: 0.0549 (0.0544)  loss_rpn_box_reg: 0.0605 (0.0621)  time: 0.2211  data: 0.0062  max mem: 8983\n",
      "Epoch: [5]  [7329/7330]  eta: 0:00:00  lr: 0.001244  loss: 0.4889 (0.5165)  loss_classifier: 0.2004 (0.2119)  loss_box_reg: 0.1731 (0.1881)  loss_objectness: 0.0527 (0.0544)  loss_rpn_box_reg: 0.0634 (0.0621)  time: 0.2122  data: 0.0070  max mem: 8983\n",
      "Epoch: [5] Total time: 0:26:49 (0.2195 s / it)\n",
      "Test:  [  0/313]  eta: 0:02:39  model_time: 0.1370 (0.1370)  evaluator_time: 0.0254 (0.0254)  time: 0.5084  data: 0.3331  max mem: 8983\n",
      "Test:  [312/313]  eta: 0:00:00  model_time: 0.1289 (0.1302)  evaluator_time: 0.0176 (0.0251)  time: 0.1577  data: 0.0063  max mem: 8983\n",
      "Test: Total time: 0:00:52 (0.1692 s / it)\n",
      "Averaged stats: model_time: 0.1289 (0.1302)  evaluator_time: 0.0176 (0.0251)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.455\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.761\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.460\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.250\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.644\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.368\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.451\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.473\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.280\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.463\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.663\n"
     ]
    }
   ],
   "source": [
    "# 使用混合精度训练\n",
    "scaler = torch.amp.GradScaler()\n",
    "max_epochs = int(max_iterations / (len(train_dataset) / train_batch_size))\n",
    "print(f\"Max epochs: {max_epochs}\")\n",
    "for epoch in range(max_epochs):\n",
    "    train_one_epoch(\n",
    "        model, optimizer, scheduler, train_dataloader, DEVICE, epoch, 100, scaler\n",
    "    )\n",
    "    # evaluate after every epoch\n",
    "    evaluate(model, val_dataloader, device=DEVICE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d88aba84",
   "metadata": {},
   "source": [
    "# Run inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cff4b15c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tf/anaconda3/envs/pytorch-cu124/lib/python3.11/site-packages/torchvision/utils.py:225: UserWarning: Argument 'font_size' will be ignored since 'font' is not set.\n",
      "  warnings.warn(\"Argument 'font_size' will be ignored since 'font' is not set.\")\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=482x640>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchvision.io.image import decode_image\n",
    "from torchvision.utils import draw_bounding_boxes\n",
    "from torchvision.models.detection import (\n",
    "    FasterRCNN_ResNet50_FPN_V2_Weights,\n",
    ")\n",
    "from torchvision.transforms.functional import to_pil_image\n",
    "\n",
    "raw_img = decode_image(\"./resources/coco_test.jpg\")\n",
    "\n",
    "model.eval()\n",
    "\n",
    "# Step 3: Apply inference preprocessing transforms\n",
    "img = val_transforms(raw_img, None)\n",
    "img = [img[0].to(DEVICE)]\n",
    "# Step 4: Use the model and visualize the predictions\n",
    "prediction = model(img)[0]\n",
    "\n",
    "# get labels for each prediction\n",
    "indices = torch.where(prediction[\"scores\"] > 0.3)\n",
    "labels = [\n",
    "    FasterRCNN_ResNet50_FPN_V2_Weights.DEFAULT.meta[\"categories\"][i]\n",
    "    for i in prediction[\"labels\"][indices]\n",
    "]\n",
    "\n",
    "box = draw_bounding_boxes(\n",
    "    raw_img,\n",
    "    boxes=prediction[\"boxes\"][indices],\n",
    "    labels=labels,\n",
    "    colors=\"red\",\n",
    "    width=4,\n",
    "    font_size=30,\n",
    ")\n",
    "to_pil_image(box.detach())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-cu124",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
